{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Build_CNN_Model.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true,"machine_shape":"hm","authorship_tag":"ABX9TyNxe+aMVh4rauNNB1e8Po4J"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"_0n-1FNXx1la"},"source":["#**Description of Notebook:**\n","The following notebook trains each model on the independently sourced training and validation **LMPred dataset**, according to the best hyperparameters during tuning. The best models (according to lowest validation loss during training) were saved and the validation accuracy/loss curves are plotted along with any reductions in learning rate during training."]},{"cell_type":"markdown","metadata":{"id":"mEu5tFNm-fk1"},"source":["#**Imports:**"]},{"cell_type":"code","metadata":{"id":"qIM1pABL-gx3"},"source":["import os\n","import re\n","import json\n","import string\n","import numpy as np\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import layers\n","from keras.models import Sequential\n","from keras.layers import Embedding, Dense, Flatten, Dropout, InputLayer, Conv2D, MaxPooling2D, BatchNormalization, Input\n","import pandas as pd\n","from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n","import matplotlib.ticker as mtick\n","from matplotlib import pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"AYvkztN-cvel"},"source":["print('Mounting google drive...')\n","from google.colab import drive\n","drive.mount('/content/drive')\n","%cd \"INSERT_GOOGLE_DRIVE_LOC\""],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zbiHCH5fmmWo"},"source":["#**Loading Data:**"]},{"cell_type":"code","metadata":{"id":"yKyW927_mqdX"},"source":["# Target Data:\n","y_train = np.array(pd.read_csv('LM_Pred_Dataset/y_train.csv', header=None))\n","y_val = np.array(pd.read_csv('LM_Pred_Dataset/y_val.csv', header=None))\n","y_test = np.array(pd.read_csv('LM_Pred_Dataset/y_test.csv', header=None))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ptGDhq6Q47pu"},"source":["# Reshaping y Data:\n","y_train_res = y_train.astype('float32').reshape((-1,1))\n","y_val_res = y_val.astype('float32').reshape((-1,1))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vdaovxHJmdbg"},"source":["def load_X_data(lang_model):\n","  if lang_model =='BERT':\n","    X_train = np.load('Embeddings/BERT/BERT_INDEP_X_TRAIN.npy')\n","    X_val = np.load('Embeddings/BERT/BERT_INDEP_X_VAL.npy')\n","  elif lang_model =='BERT_BFD':\n","    X_train = np.load('Embeddings/BERT_BFD/BERT_BFD_INDEP_X_TRAIN.npy')\n","    X_val = np.load('Embeddings/BERT_BFD/BERT_BFD_INDEP_X_VAL.npy')\n","  elif lang_model =='T5XL_UNI':\n","    X_train = np.load('Embeddings/T5XL_UNI/T5XL_UNI_INDEP_X_TRAIN.npy')\n","    X_val = np.load('Embeddings/T5XL_UNI/T5XL_UNI_INDEP_X_VAL.npy')\n","  elif lang_model =='T5XL_BFD':\n","    X_train = np.load('Embeddings/T5XL_BFD/T5XL_BFD_INDEP_X_TRAIN.npy')\n","    X_val = np.load('Embeddings/T5XL_BFD/T5XL_BFD_INDEP_X_VAL.npy')\n","  elif lang_model =='XLNET':\n","    X_train = np.load('Embeddings/XLNET/XLNET_INDEP_X_TRAIN.npy')\n","    X_val = np.load('Embeddings/XLNET/XLNET_INDEP_X_VAL.npy')\n","\n","  return X_train, X_val"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UOqg_pJEmqlT"},"source":["#**Model Creation:**"]},{"cell_type":"markdown","metadata":{"id":"GGrikXGIpT9Z"},"source":["##**One Layer Model:**"]},{"cell_type":"code","metadata":{"id":"80b0chvrpSlp"},"source":["def train_model(X_train, y_train, X_val, y_val, model_path, plots_path, epochs, batch_size, use_tpu, filter, k_size, k_init, pool_strides, lr, opt):\n","\n","    def create_model():\n","      # Create model\n","      model = Sequential()\n","\n","      # Adding Convolutional (2D) Layer:\n","      model.add(Conv2D(filters=filter, kernel_size=k_size, activation='relu', \n","                      strides = 1, kernel_initializer=k_init, padding='same',\n","                      input_shape = (255, 1024, 1)))\n","\n","      # Adding Max Pooling Layer:\n","      model.add(MaxPooling2D(pool_size=2, strides=pool_strides))\n","\n","      # Adding Batch Normalisation Layer:\n","      model.add(BatchNormalization(axis=1, momentum=0.99, epsilon=0.001))\n","\n","      model.add(Flatten())\n","\n","      # Adding the dense layer using Sigmoid function to predict in range [0, 1]:\n","      model.add(Dense(1, activation='sigmoid'))\n","      \n","      # Choosing optimizer and compiling model:\n","      if opt == 'Adam':\n","        optimizer = keras.optimizers.Adam(learning_rate=lr)\n","      else:\n","        optimizer = keras.optimizers.SGD(learning_rate=lr)\n","\n","      model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n","\n","      return model\n","\n","    # Assigning to TPU strategy:\n","    # Disabling displayed debugging logs:\n","    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n","    if use_tpu:\n","        # Create distribution strategy:\n","        tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n","        tf.config.experimental_connect_to_cluster(tpu)\n","        tf.tpu.experimental.initialize_tpu_system(tpu)\n","        strategy = tf.distribute.TPUStrategy(tpu)\n","\n","        # Create model:\n","        with strategy.scope():\n","            model = create_model()\n","    else:\n","        !nvidia-smi -L\n","        model = create_model()\n","\n","    model.summary()\n","\n","    # Using callbacks for the model:\n","    earlyStopping = EarlyStopping(monitor='val_loss', patience=12, verbose=0, mode='min')\n","    mcp_save = ModelCheckpoint(model_path, save_best_only=True, monitor='val_loss', mode='min')\n","    reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, verbose=1, min_delta=1e-4, mode='min')\n","\n","    # Reshaping X and y Data for Model Input:\n","    seq_height = 255\n","    seq_width = 1024\n","\n","    X_train_res = X_train.reshape(X_train.shape[0], seq_height, seq_width, 1)\n","    X_val_res = X_val.reshape(X_val.shape[0], seq_height, seq_width, 1)\n","\n","    y_train_res = y_train.astype('float32').reshape((-1,1))\n","    y_val_res = y_val.astype('float32').reshape((-1,1))\n","\n","\n","    # Fitting the model:\n","    history = model.fit(X_train_res,\n","              y_train_res,\n","              validation_data=(X_val_res, y_val_res),\n","              epochs=epochs,\n","              batch_size=batch_size,\n","              callbacks=[earlyStopping, mcp_save, reduce_lr_loss])\n","    \n","    # Plotting Accuracy and Loss Curves:\n","    fig, ax = plt.subplots(1,2, figsize=(18,6), dpi=80)\n","    train_metrics = ['accuracy', 'loss']\n","    val_metrics = ['val_accuracy', 'val_loss']\n","    titles = ['Model Accuracy', 'Model Loss vs. Learning Rate']\n","    y_labels = ['Accuracy', 'Loss']\n","    leg_loc = ['upper left', 'upper right']\n","\n","    for i in range(2):\n","      \n","      ax[i].plot(history.history[train_metrics[i]])\n","      ax[i].plot(history.history[val_metrics[i]])\n","      \n","      if i == 1:\n","        ax2=ax[i].twinx()\n","        ax2.plot(history.history['lr'], color= 'magenta', linestyle='dotted')\n","        ax2.set_ylabel('Learning Rate')\n","        ax2.legend(['Learning Rate'], fancybox=True, framealpha=1, shadow=True, borderpad=1, bbox_to_anchor=(1.0, 0.85))\n","        ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.e'))\n","\n","      \n","      ax[i].set_title(titles[i], fontsize=12, fontweight='bold')\n","      ax[i].set_ylabel(y_labels[i])\n","      ax[i].set_xlabel('Epoch')\n","      ax[i].legend(['Train', 'Val'], loc=leg_loc[i], fancybox=True, framealpha=1, shadow=True, borderpad=1)\n","\n","    plt.savefig(plots_path, bbox_inches='tight')\n","    plt.show()\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"t6Xrpct-pWyL"},"source":["##**Two Layer Model:**"]},{"cell_type":"code","metadata":{"id":"Kqx6WSDHVH7d"},"source":["def train_complex_model(X_train, y_train, X_val, y_val, model_path, plots_path, epochs, batch_size, use_tpu, filter, k_size, k_init, pool, pool_strides, filter2, k_size2, pool2, p_strides2, dense1, dense2, dropout, lr, opt):\n","\n","    def create_model():\n","      # Create model\n","      model = Sequential()\n","\n","      # Adding Convolutional (2D) Layer:\n","      model.add(Conv2D(filters=filter, kernel_size=k_size, activation='relu', \n","                      strides = 1, kernel_initializer=k_init, padding='same',\n","                      input_shape = (255, 1024, 1)))\n","\n","      # Adding Max Pooling Layer:\n","      model.add(MaxPooling2D(pool_size=pool, strides=pool_strides))\n","\n","      # Adding Batch Normalisation Layer:\n","      model.add(BatchNormalization(axis=1, momentum=0.99, epsilon=0.001))\n","\n","      # Adding Convolutional (2D) Layer:\n","      model.add(Conv2D(filters=filter2, kernel_size=k_size2, activation='relu', \n","                      strides = 1, kernel_initializer=k_init, padding='same',\n","                      input_shape = (255, 1024, 1)))\n","\n","      # Adding Max Pooling Layer:\n","      model.add(MaxPooling2D(pool_size=pool2, strides=p_strides2))\n","      \n","      # Adding Batch Normalisation Layer:\n","      model.add(BatchNormalization(axis=1, momentum=0.99, epsilon=0.001))\n","\n","      model.add(Flatten())\n","\n","      model.add(Dense(dense1, activation='relu'))\n","      model.add(Dropout(rate=dropout))\n","\n","      model.add(Dense(dense2, activation='relu'))\n","      model.add(Dropout(rate=dropout))\n","\n","      # Adding the dense layer using Sigmoid function to predict in range [0, 1]:\n","      model.add(Dense(1, activation='sigmoid'))\n","      \n","      # Choosing optimizer and compiling model:\n","      if opt == 'Adam':\n","        optimizer = keras.optimizers.Adam(learning_rate=lr)\n","      else:\n","        optimizer = keras.optimizers.SGD(learning_rate=lr)\n","        \n","      model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n","\n","      return model\n","\n","    # Assigning to TPU strategy:\n","    # Disabling displayed debugging logs:\n","    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n","    if use_tpu:\n","        # Create distribution strategy:\n","        tpu = tf.distribute.cluster_resolver.TPUClusterResolver()\n","        tf.config.experimental_connect_to_cluster(tpu)\n","        tf.tpu.experimental.initialize_tpu_system(tpu)\n","        strategy = tf.distribute.TPUStrategy(tpu)\n","\n","        # Create model:\n","        with strategy.scope():\n","            model = create_model()\n","    else:\n","        !nvidia-smi -L\n","        model = create_model()\n","\n","    model.summary()\n","\n","    # Using callbacks for the model:\n","    earlyStopping = EarlyStopping(monitor='val_loss', patience=12, verbose=0, mode='min')\n","    mcp_save = ModelCheckpoint(model_path, save_best_only=True, monitor='val_loss', mode='min')\n","    reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=4, verbose=1, min_delta=1e-4, mode='min')\n","\n","    # Reshaping X and y Data for Model Input:\n","    seq_height = 255\n","    seq_width = 1024\n","\n","    X_train_res = X_train.reshape(X_train.shape[0], seq_height, seq_width, 1)\n","    X_val_res = X_val.reshape(X_val.shape[0], seq_height, seq_width, 1)\n","\n","    y_train_res = y_train.astype('float32').reshape((-1,1))\n","    y_val_res = y_val.astype('float32').reshape((-1,1))\n","\n","\n","    # Fitting the model:\n","    history = model.fit(X_train_res,\n","              y_train_res,\n","              validation_data=(X_val_res, y_val_res),\n","              epochs=epochs,\n","              batch_size=batch_size,\n","              callbacks=[earlyStopping, mcp_save, reduce_lr_loss])\n","    \n","    # Plotting Accuracy and Loss Curves:\n","    fig, ax = plt.subplots(1,2, figsize=(18,6), dpi=80)\n","    train_metrics = ['accuracy', 'loss']\n","    val_metrics = ['val_accuracy', 'val_loss']\n","    titles = ['Model Accuracy', 'Model Loss vs. Learning Rate']\n","    y_labels = ['Accuracy', 'Loss']\n","    leg_loc = ['upper left', 'upper right']\n","\n","    for i in range(2):\n","      \n","      ax[i].plot(history.history[train_metrics[i]])\n","      ax[i].plot(history.history[val_metrics[i]])\n","      \n","      if i == 1:\n","        ax2=ax[i].twinx()\n","        ax2.plot(history.history['lr'], color= 'magenta', linestyle='dotted')\n","        ax2.set_ylabel('Learning Rate')\n","        ax2.legend(['Learning Rate'], fancybox=True, framealpha=1, shadow=True, borderpad=1, bbox_to_anchor=(1.0, 0.85))\n","        ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.e'))\n","\n","      \n","      ax[i].set_title(titles[i], fontsize=12, fontweight='bold')\n","      ax[i].set_ylabel(y_labels[i])\n","      ax[i].set_xlabel('Epoch')\n","      ax[i].legend(['Train', 'Val'], loc=leg_loc[i], fancybox=True, framealpha=1, shadow=True, borderpad=1)\n","\n","    plt.savefig(plots_path, bbox_inches='tight')\n","    plt.show()\n","    "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"XcA1otrER-FM"},"source":["#**Model Training:**"]},{"cell_type":"markdown","metadata":{"id":"3G1_qCr6uQgI"},"source":["##**BERT:**"]},{"cell_type":"code","metadata":{"id":"Z0ur9VZ5mxR3"},"source":["X_train, X_val = load_X_data('BERT')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"gTT33W7GDunt","executionInfo":{"status":"ok","timestamp":1628617666511,"user_tz":-60,"elapsed":1826967,"user":{"displayName":"Will Dee","photoUrl":"","userId":"18339245303983317021"}},"outputId":"f8cb354c-c1a6-44ef-86b0-9377b11d60c9"},"source":["BERT_filepath = 'Keras_Models/BERT_best_model.epoch{epoch:02d}-loss{val_loss:.2f}.hdf5'\n","BERT_Plots_Path = 'Training_Plots/INDEP/BERT_Best_Model_Plot.png'\n","train_model(X_train, y_train_res, X_val, y_val_res, BERT_filepath, BERT_Plots_Path, 30, 8, False, 320, 11, 'RandomNormal', 8, 0.0001, 'SGD')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-52a51320-971d-dcb8-6340-bcb3526c91ce)\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 255, 1024, 320)    39040     \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 32, 128, 320)      0         \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 32, 128, 320)      128       \n","_________________________________________________________________\n","flatten (Flatten)            (None, 1310720)           0         \n","_________________________________________________________________\n","dense (Dense)                (None, 1)                 1310721   \n","=================================================================\n","Total params: 1,349,889\n","Trainable params: 1,349,825\n","Non-trainable params: 64\n","_________________________________________________________________\n","Epoch 1/30\n","376/376 [==============================] - 98s 182ms/step - loss: 1.4176 - accuracy: 0.5746 - val_loss: 0.7112 - val_accuracy: 0.5811\n","Epoch 2/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.7396 - accuracy: 0.7376 - val_loss: 0.5588 - val_accuracy: 0.7660\n","Epoch 3/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.5338 - accuracy: 0.8142 - val_loss: 1.0694 - val_accuracy: 0.6549\n","Epoch 4/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.5496 - accuracy: 0.7835 - val_loss: 0.6291 - val_accuracy: 0.7985\n","\n","Epoch 00004: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n","Epoch 5/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2813 - accuracy: 0.8895 - val_loss: 0.4266 - val_accuracy: 0.8457\n","Epoch 6/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2377 - accuracy: 0.9064 - val_loss: 0.3973 - val_accuracy: 0.8424\n","Epoch 7/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2397 - accuracy: 0.8983 - val_loss: 0.3864 - val_accuracy: 0.8497\n","Epoch 8/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2439 - accuracy: 0.9022 - val_loss: 0.3874 - val_accuracy: 0.8444\n","Epoch 9/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2531 - accuracy: 0.9016 - val_loss: 0.3741 - val_accuracy: 0.8564\n","Epoch 10/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2265 - accuracy: 0.9098 - val_loss: 0.3741 - val_accuracy: 0.8544\n","Epoch 11/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2369 - accuracy: 0.9043 - val_loss: 0.3795 - val_accuracy: 0.8537\n","\n","Epoch 00011: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n","Epoch 12/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2410 - accuracy: 0.9004 - val_loss: 0.3755 - val_accuracy: 0.8557\n","Epoch 13/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2023 - accuracy: 0.9275 - val_loss: 0.3768 - val_accuracy: 0.8557\n","\n","Epoch 00013: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.\n","Epoch 14/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2288 - accuracy: 0.9174 - val_loss: 0.3760 - val_accuracy: 0.8544\n","Epoch 15/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2243 - accuracy: 0.9097 - val_loss: 0.3739 - val_accuracy: 0.8551\n","Epoch 16/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2423 - accuracy: 0.9042 - val_loss: 0.3754 - val_accuracy: 0.8557\n","Epoch 17/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2305 - accuracy: 0.9103 - val_loss: 0.3746 - val_accuracy: 0.8551\n","\n","Epoch 00017: ReduceLROnPlateau reducing learning rate to 1.0000000116860975e-08.\n","Epoch 18/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2296 - accuracy: 0.9133 - val_loss: 0.3750 - val_accuracy: 0.8551\n","Epoch 19/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2075 - accuracy: 0.9241 - val_loss: 0.3754 - val_accuracy: 0.8551\n","\n","Epoch 00019: ReduceLROnPlateau reducing learning rate to 9.999999939225292e-10.\n","Epoch 20/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2183 - accuracy: 0.9123 - val_loss: 0.3749 - val_accuracy: 0.8551\n","Epoch 21/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2088 - accuracy: 0.9237 - val_loss: 0.3754 - val_accuracy: 0.8551\n","\n","Epoch 00021: ReduceLROnPlateau reducing learning rate to 9.999999717180686e-11.\n","Epoch 22/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2145 - accuracy: 0.9136 - val_loss: 0.3747 - val_accuracy: 0.8544\n","Epoch 23/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2386 - accuracy: 0.9117 - val_loss: 0.3744 - val_accuracy: 0.8544\n","\n","Epoch 00023: ReduceLROnPlateau reducing learning rate to 9.99999943962493e-12.\n","Epoch 24/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2310 - accuracy: 0.9103 - val_loss: 0.3746 - val_accuracy: 0.8551\n","Epoch 25/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2265 - accuracy: 0.9074 - val_loss: 0.3751 - val_accuracy: 0.8551\n","\n","Epoch 00025: ReduceLROnPlateau reducing learning rate to 9.999999092680235e-13.\n","Epoch 26/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2194 - accuracy: 0.9195 - val_loss: 0.3754 - val_accuracy: 0.8551\n","Epoch 27/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2253 - accuracy: 0.9130 - val_loss: 0.3748 - val_accuracy: 0.8544\n","\n","Epoch 00027: ReduceLROnPlateau reducing learning rate to 9.9999988758398e-14.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x480 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"w0lYzYLRRr3W"},"source":["##**BERT BFD:**"]},{"cell_type":"code","metadata":{"id":"v8QomUZFBLuY"},"source":["X_train, X_val = load_X_data('BERT_BFD')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"NDg160rRNiF1","executionInfo":{"status":"ok","timestamp":1628619813334,"user_tz":-60,"elapsed":1364152,"user":{"displayName":"Will Dee","photoUrl":"","userId":"18339245303983317021"}},"outputId":"ef7690c9-71e8-4d83-8c96-11c4e8ba516c"},"source":["BERT_BFD_filepath = 'Keras_Models/BERT_BFD_best_model.epoch{epoch:02d}-loss{val_loss:.2f}.hdf5'\n","BERT_BFD_Plots_Path = 'Training_Plots/INDEP/BERT_BFD_Best_Model_Plot.png'\n","train_model(X_train, y_train_res, X_val, y_val_res, BERT_BFD_filepath, BERT_BFD_Plots_Path, 30, 8, False, 320, 11, 'RandomNormal', 8, 0.0001, 'SGD')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-52a51320-971d-dcb8-6340-bcb3526c91ce)\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 255, 1024, 320)    39040     \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 32, 128, 320)      0         \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 32, 128, 320)      128       \n","_________________________________________________________________\n","flatten (Flatten)            (None, 1310720)           0         \n","_________________________________________________________________\n","dense (Dense)                (None, 1)                 1310721   \n","=================================================================\n","Total params: 1,349,889\n","Trainable params: 1,349,825\n","Non-trainable params: 64\n","_________________________________________________________________\n","Epoch 1/30\n","376/376 [==============================] - 98s 182ms/step - loss: 0.7231 - accuracy: 0.7081 - val_loss: 0.4656 - val_accuracy: 0.8577\n","Epoch 2/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.3079 - accuracy: 0.8772 - val_loss: 0.4229 - val_accuracy: 0.8371\n","Epoch 3/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.2245 - accuracy: 0.9066 - val_loss: 0.3811 - val_accuracy: 0.8650\n","Epoch 4/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.1861 - accuracy: 0.9402 - val_loss: 0.4247 - val_accuracy: 0.7999\n","Epoch 5/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.1725 - accuracy: 0.9417 - val_loss: 0.3425 - val_accuracy: 0.8790\n","Epoch 6/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.1350 - accuracy: 0.9605 - val_loss: 0.3270 - val_accuracy: 0.8797\n","Epoch 7/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.1315 - accuracy: 0.9646 - val_loss: 0.3302 - val_accuracy: 0.8703\n","Epoch 8/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.1166 - accuracy: 0.9731 - val_loss: 0.3201 - val_accuracy: 0.8843\n","Epoch 9/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.1113 - accuracy: 0.9688 - val_loss: 0.3291 - val_accuracy: 0.8797\n","Epoch 10/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.1030 - accuracy: 0.9714 - val_loss: 0.3328 - val_accuracy: 0.8763\n","\n","Epoch 00010: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n","Epoch 11/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.0840 - accuracy: 0.9867 - val_loss: 0.3274 - val_accuracy: 0.8823\n","Epoch 12/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.0778 - accuracy: 0.9917 - val_loss: 0.3236 - val_accuracy: 0.8803\n","\n","Epoch 00012: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n","Epoch 13/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.0800 - accuracy: 0.9881 - val_loss: 0.3229 - val_accuracy: 0.8810\n","Epoch 14/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.0837 - accuracy: 0.9837 - val_loss: 0.3226 - val_accuracy: 0.8803\n","\n","Epoch 00014: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.\n","Epoch 15/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.0806 - accuracy: 0.9836 - val_loss: 0.3234 - val_accuracy: 0.8803\n","Epoch 16/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.0815 - accuracy: 0.9871 - val_loss: 0.3225 - val_accuracy: 0.8797\n","\n","Epoch 00016: ReduceLROnPlateau reducing learning rate to 1.0000000116860975e-08.\n","Epoch 17/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.0856 - accuracy: 0.9826 - val_loss: 0.3227 - val_accuracy: 0.8803\n","Epoch 18/30\n","376/376 [==============================] - 66s 177ms/step - loss: 0.0795 - accuracy: 0.9862 - val_loss: 0.3237 - val_accuracy: 0.8803\n","\n","Epoch 00018: ReduceLROnPlateau reducing learning rate to 9.999999939225292e-10.\n","Epoch 19/30\n","376/376 [==============================] - 66s 176ms/step - loss: 0.0759 - accuracy: 0.9879 - val_loss: 0.3222 - val_accuracy: 0.8803\n","Epoch 20/30\n","376/376 [==============================] - 67s 177ms/step - loss: 0.0838 - accuracy: 0.9812 - val_loss: 0.3224 - val_accuracy: 0.8803\n","\n","Epoch 00020: ReduceLROnPlateau reducing learning rate to 9.999999717180686e-11.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABNYAAAGrCAYAAAAM8RoHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAMTQAADE0B0s6tTgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXgV5dnH8e99ThJCEsKSAGFN2ET2uAuColJxX1HcpcVKbW2tWl9b21prba1L1aq12qqFWnFXlLqjokUQRQQUEEEI+5awBBKynfO8f8wEDiFACEkmy+9zXbnOmZln5twzOYRn7nkWc84hIiIiIiIiIiIiByYUdAAiIiIiIiIiIiINkRJrIiIiIiIiIiIi1aDEmoiIiIiIiIiISDUosSYiIiIiIiIiIlINSqyJiIiIiIiIiIhUgxJrIiIiIiIiIiIi1aDEmoiIiIiIiIiISDUosSYiNcrMhpuZ83+yDmC/2/19cmotOBEREZEYqrdIOTMb7/9OpwYdi4g0LEqsiTQBZjY1ptL4ZoVto2O2HVClMihmFjazlTEx/y7omERERKRmNJZ6i5llxcQ5Juh4glAhcTk86Hj2413gr8BLdfFhFb4f5T/bzexzM7ugGsfL8Y9xey2EKyL7oMSaSNNzmpn1j1m+ObBIqu8MoHPM8lgzCwcVTCwzizMzCzoOERGRRqIx1FskIGaWUNWyzrmJzrmfO+ceqc2Y9uI94G/ASuBI4DkzOySAOESkGpRYE2la8vzXXwCY2YnAETHrd2NmZ5rZx2aWa2abzexTM7uoQpmzzexrM9thZv8D+ldyHDOzy8xspn+cjWb2lpkdXs3zGOe/zgKiQBfgtAqfmWRmvzKzr/ynf1vNbJqZJVVx+x5PmCuuM7MxMeuuNLOlQDHQ0sx+7F+XrWZWZmYbzOx5M+scc7w4M/uRmX1hZlv8OGaZWaaZ3ewf97vYRJ2ZLfPX31DNayciItJQNJZ6y16ZWbyZ/dzM5prZNjNbY2b/NbMjY8ocamZv+udV7JeZYma9q7K9ks/8u1+XeC9mXaqZFfjrL/DrKPea1wqqyL8Os83sR7VwDZLN7DYzm+/HsNzMnjCzjJgyValXlbd0fMHMnjKzLcDztnvLsF/49b0iM1tkZmfE7L9HV9CY/f7gfwd2+PH9IKaMmVenXO7X5Z4xswfswLoKT3TOXQdc4i/HAQNjPuOfZrbUvz4l/u/lPvMTh2bmgEy/+O9iP9v/jl1nZnPMLN//frxgZj2rGJuI7IcSayJNyzRgHnCpmXVi11Pfv1UsaGY/BCYDw4DpwBTgaLwKys1+mcOBV4F+wBxgKXB3JZ97B/AfoCfwCjAVGAlMP9BKqpl1BU71Fx/wjwW7km2YmQFvAn8CegNv+XF2ARL2t/1A4onxKPA/4GUgAvQAlgPPAU8C24CL8K5DuaeAvwOH4f1ungOSgdbAE0Ah0B042T+vY4AsoAT4dzXjFBERaSgafL2lCibg1Wd64dUhluK1zJ9uZkP8Mo/hPUBchFenmAEcCnSo4vaK/u6/nmRmXfz3FwFJwGrgNeByvIRmc2A83rWNAEcdzMlWFFMn+z1eMunfeK22xgIzzCzFL1qVelW5UcBg4HlgdoVtfwJygO+AQ4BnYj5jX36NVy+bC3QFHjezbv62n/rH7Yr3vWvjrzsgZhYHHOsvRvzPKtcL+ALv+/Is0Aq4CbjV3/5XvGsCMNNffspffgp4GEj3950LXAh8FvP7F5GDoMSaSNNzLxDPrkrYFODLSsrd4r9OdM6d7Zy7kF0Vsf/zX6/B+zuSAwx1zl0F3BZ7EP9J2o3+4pd4/+mvBtYAzTjwisfV/mcW4FX8JvrrT4t5ajkEOMF/f4Vz7kLn3Bi8JFV+FbZXx8XOuauccxc557bhVcCeBjbgVcS+8ssdb2bN/Viv8Nf9yjl3pnPuaudcH+Br59xmdiXPrvFfR/uvrzrnKn1aLyIi0sg09HrLXvl1gfIWStf7dZFhwHy8cy6PI9F//QQvkTTOOdcZ74FeVbbvxjk3Dy/5GAKu9FeP8V//6ZwriznmcmASXuLrGGIeZNaQIcDx/vsZeC3/ZwMO72Fi+Vhj+6xXVTjmauBw59w459wfKmy7wzl3OXCVv9wS7yHr/jzlnLsAONNfjgPKk6w/9l+fdc6d65w7Da9r54H4F1CK951dD5zpnFscs/1i/5j5wCZgib/+VADn3M/99QBv+11a7/AT0pf76z8DduAlYLfhPcjd2fJORKovLugARKTOPYf3VK28YnAv3hPKisqbk8+LWVdeiUk3r8tkV3/5G+dcxH//dYXjpMcc/2T/J1ZXqsi8cdTG+ouvOecKzOxlvCfXzfCSbrfjVcTK7axUlsdouw90vMf2vXz2/v5eTqlQdipeBXSPQwHt2H2MuN0qvn6FFuAh4EfAOWbWHu/pInit2URERJqCBltvqYLMmPfzAJxzzsy+xmtVV/5ZP8VrgXQDfqs9M5uH94BuXhW2V+bveEmtq8zsBeA4oAz4p799Al7rtAvwWvYDbAbuBO6v7glXIvYaXFXJ9q5VrFctj1n3iXNux14+b6b/GvuAskUV4tzXfuW/p9gWZnPZ1cOiKt4D1uE9RG0PnAu8DTvrrZ/jfTcrar+f48Ze3/Mq2V6T32eRJkst1kSaGD9p86C/OM859+5eipZXUAbErCsfhyTXOVcIrPCXD7VdkwdUHKskF+/JIsCNzjkr/wFSgcsOIPwzgY7++0v98SQ24yXVYNckBjkx+wwtf2PebKKhKmwHr0UceM35IWaci8o454piFvuyq/J3Ad5DjEtjthu7VwCHErvRT+I55xbizVCVgFfR7YzXReT9fcUiIiLSWDTwesv+xNYFBsDOrpH9/HXl8X7lnBsCpPjbXsSrl/y2itsr8yLeufZiV8u+15xza/z3UefcWOdcK7yhMq7Ca+F0n5m18GPtYd74bpUlfKpqRcz7wytc7y7AfVStXhWriL0r9V/dAcZZCl7is5Jt5b/HvjHr9llvrMRE59yVwLX+8jgzO8V/fzZeUm070N2/Nv/wt8Wee/mD2dh7/JUx78+vcH3T0WQgIjVCLdZEmqbH8J5gLttHmfvwKlqX+RWoYnY1x7/Pf30Cr1tFFjDNzBaxq7siAM65EjN7CPglcLeZDQM24j1BGwb8BG/sjqoo736wid1beaXgPVHuDJwO/Bf4GK9rwdNmNgqvyfsJeDMtTd/P9i14zeVPBG6o0FqsKjbiVW7i8CosZ7LrSTsAzrlVZvY03tPku8xsKN6TymPwKq9z/KJ/BU4BzvKXn9pLpU5ERKSxaqj1lli/td0H/v/WOXelmT3vx/BXMzsOb1y3/nj1iPKE4n/NLBGv+98OvPHDYFfXv/1t34NzrtjM/oVXTylvlfdoTJFLzOx3ePWhXHYljQrwri14D/oy8bqJ3r7fKwCPmlnskBsfAb/Cq5cNAaaY2WS8pNcheOON9aAK9aqAPYrXy+AKM0vGG5duZDWPNR74OV6i9c/mTTBRnuxMAf5iZlG8cfgqysG7XleZWStgtnNuvJk9h9eV9D9m9hre77AH3oPdU9g1XrGIVJNarIk0Qc65QufcFOfcd/so8xhek/FP2PUf7yzgMufc3X6ZWXiV1gV4A/D3ZtcgqrFuxUsWzQaG44310ANvUNkZVYnZzDLZVUm5yx/D4lzn3Ll+bN/428b5iafT8Mbj+BYv2TYKb8yKkv1t94/zY7yKXhu8ysudVYkTwDm3Fm+skmV41+UQKn9q/AP/c+bgJfUuxXsiujmmzFt+jOANZPuvqsYhIiLSGDTEeksluuM9PCv/KW/RdCXeJAHf4T3E64n3f/9Q59w0v8wUvG6H5+ENiRHCS8D8sorb9+YxdrXc+sY590HMtm/wWsmfAPwQyMZ7KHm2c66E6unD7tegt3Muitdl8nd4Dxgv9M8jGa/Lae4B1KuC8je878wqvHPZCjzubyvb206V8a9H+bkdhpd0fRkvOZyL970uppIJPPDqtfOADLzuweXJx6uA6/Hqk2fgJdky8Fq9LTqQ+ESkcqaGDyIi9ZuZ/RGvwva6c+6coOMREREREY+ZxQMJzrmCmHUf4iVl33XOVbf1mog0EOoKKiJST5nZsXjdUb/vr7onwHBEREREZE9pwDwzewlYizcRxHC81mp3BxiXiNQRJdZEROqv2K4RP3HOfRJwPCIiIiKyu0K8GWhHAy3xuoK+BfzZOfdxkIGJSN1QV1AREREREREREZFq0OQFIiIiIiIiIiIi1aDEmoiIiIiIiIiISDU06jHWmjVr5tq2bRt0GCIiIlJLVq9eXeKcaxZ0HLI7M3OhkJ7fioiINFbRaBTnnAUdR33QqBNrbdu2ZdWqVUGHISIiIrXEzDYGHYPsKRQKEYlEgg5DREREaomZ6T96nx4lioiIiIiIiIiIVIMSayIiIiIiIiIiItWgxJqIiIiIiIiIiEg1NOox1vZHY38cuHA4HHQIIiIiIiIiIgdE9/8HTvf/VdMkE2tFRUUsXryYkpKSoENpcBISEujVqxeJiYlBhyIiIiIiIiKyT7r/rz7d/1dNk0ysLV68mNatW5ORkYGZZoetKucca9eu5ZtvvmHAgAHKXouIiIiIiEi9pvv/6om9/+/SpQtpaWlBh1RvNbnEWiQSoaSkhIyMDOLimtzpH7QOHTqwYcMGPvroI0444QQl10RERERERKRe0v3/wSm//584cSKnnXYaPXv2DDqkeqnJTl6gTHX1lF+3uXPnkpOTE2wwIiIiIiIiIvuh+//qKb9uSUlJvPXWWxQXFwccUf3UZBNrcnBCoRAFBQVBhyEiIiIiIiIitSg5OZnS0lIKCwuDDqVeUmJNqs05F3QIIiIiIiIiIlIHlAOonBJrIiIiIiIiIiIi1aDEmoiIiIiIiIiI1Cgze8jMcszMmVl2NY9xjJnNNbNvzewDM+tUSZnfH8xnHCwl1kREREREREREpKa9BAwFlldnZzMLAc8AP3fOHQK8CTxYoczRwFHV/YyaoMSaiIiIiDQIzjmKSiNBhyEiIiJV4Jz72Dm3quJ6MzvKb302y8y+NLML93KII4Ay59yH/vLjwFlmlugfJwl4BBhXG/FXlRJrIiLV4Jwjb3sxX6/eyvsL1/PMzOXc/+4ibn5xLlc8OZNTHviIgbe/w4Db3+F793/EFU/O5BcvzuUv7y7iP58uZ8qC9Xy9eiu524uJRjUIqIjI/qzduoMrx3xGQmLIq0KXywb6xyw/BsQBH8asiwdiq+zX+2XW+8sL/eXbY8qMANrFLL/il3khZl1H4MSY5T/4Zb7yl/P85Z/ElLnEXxf1lz/2l3VOOqf6eE4iIjXMzFoB/wAuc84dCXwP+EtlXTyBrsS0RHPObQPy8f4KAtwD/N05t7J2o963uCA/XPYtO9vrHlxSUsKiRYsYMGAAAL179+b555+v0jFef/11PvzwQx544IFai1OksSkpi7I+v4h1+UWs21rkvd9axNr8ItZv9dZvyC+mJBKtdP/m8WE6tEykX8eWmMG6/CK+WL6ZwpLKW1nEh412LRLJaJlIRuqu1/bly6mJtG/ZjGZx4do8bRGReq1tSjNyE4qZk72Fw7u13rXhBHYlCgCygLOAtJh1ZwFHxiz399c185dT/OXeMWWGAB1iljv4ZTrGrDsFiL0NOMQvk+ovJ/jLA2LKHAEUxSyn+WW6xazTOemc6ss5iUidaYD3/yEzi22Ndr9z7v4q7DcE6A68ZWax63sDq6v64Wb2PSDTOXddVfepLdaYp0vt3LmzW7Vq91aHkUiEOXPmkJ2dTTjcMG5Sc3JyyM7OZsuWLXtsKysrIy6u7vKj5ddv2rRpDB8+nEGDBtXZZ4vUpEjU8d6CdXy7fjvr/ITZWj+JlldQstf90pIT9kx8xby2T00kNTGOCv9J4JxjW3HZzsTc2q27knTr83d9du72vX92m+QE2qcmkpHazP/M5rRJjqcs6iiNRCmNeK9lEUdpNEppmaMsGt1jW0kkSlkkSlnUUVIWrWT/Xe8jUUcoZCSEQ8SFjfhwiLiQkRDnvcaFQ7tti99ZJuZ92CqU8baVl3GwK46yKKXRPeMo819L/TJl0SglEeedR/k5+edc6p+zt4/DzEt2JiWEaZ7gv8bH+a+71nnb43a9jw+TlBBH84TQzvK7jhFHOGR7/I5KyqLsKIlQWFpGYUmEHSURdpRG/PfeuvL1hX65op3vy9eX7dpeEqG4LIKZER8y4v3rvtt1rHDdE+LKr2359j2ve8Jux/F+B7G//53Xdud3JPbaut1/N5Ve913lSiLe93DWr0cQF675hvJmtto517nGDywHJRwOu0ik5rtsnnjfVOJCxns3nlDjxxYRkcZH9/8Hp/z6LV68mDVr1jBmzBjatGkDgJlFnHNVCsbMcoBznXNzzOwM4NfOuSGVlPslcLG/eAuwCXjaOXeov70FkAu0BH4HXAmU+uU7AxuAcc65ydU74+pRi7UGKCsri9GjR/Phhx/Sq1cv/vKXv3DJJZeQn59PUVERJ554Ig899BChUIjx48czadIkJk2axNSpU7nuuus4/vjj+eSTTygrK2PChAkceeSR+/9QkUZk5tI8fj95AQvW5u9clxAO0S61Gd3bJjO4R9quhFl5Ei01kXap1W81ZmakJsaTmhhPr/Yt9lqupCzKhm1eC7nYFnPlibd1+UV8smT7XlvLVUU4ZF5yrLJkVzhE84Q4UsNembhQiIhzuyVbikojbCsq85Mq5UkUL6FSVkfdWstj35XkCxEfZ8T7CaXm8eGd5xR17ExwbS4s2ZnYOthYE+JCJCV4n1NUWrPHTPITfm2SE0iMD+EcuyU/i8uibCsqoywamyx1O38ndSF+j0Trrt9BXNhIjA/7iVfve6Qez1ITstKS+OS7PKJ+0l9EasEbeF1LHwSODTgWEWmM9//TgW5mNsI5NwXAn81zgXPuz8Cfywv6kxfEm9mJ/jhr44DJzrki4Ff+T3nZHPzkXd2dikeJtX24esLnLM8rrJVjZ6Yl8cRVR1V7/7y8PGbOnImZUVRUxOTJk0lJSSESiXDOOefwwgsvcPHFF++x3zfffMOTTz7Jo48+ymOPPcavf/1r3nnnnYM5FZEGY+WmQu56ayFvfrWOhHCIcSd056yBHenQMpE2yQl7tDILQkJciM6tk+jcOmmvZZxzbCooYV1+EVsLS4mrrFVYqJJWZGEv6VGbN6PRqNuZbNvZci6yZ/IntkWTmVXSui20W+ImPi7kJ82McMhq5HdV3rrMa1HmtzCr0LpsR2lkt9ZjO1ud+et3lEQoiURJjK+sVZvXsi0xPlxJK7jd1zX3X2uiRZdzXku92JZkFa/7zmSo3wIQY4/rXrF1YvnvoDyhWR/+vUjTk5mWzIeLNrI2v4hOrZoHHY5I41QErAS2Bh2ISN2qzft/OLgcQEO9/zezx4EzgAzgHTPb5pzr6bdau8/M/oI3GuUK4NyK+zvnomZ2OfC4P2HBGuCKOjuBKlJirYEaM2bMzpuaaDTKLbfcwrRp03DOsWHDBvr371/pP6yePXtyzDHHADB48GDuu+++Oo1bJAgFxWU8OnUJ//zfMkrKoozs155bT+9DZlpy0KFVi5mRltKMtJRm+y9cx0IhI8FPxtR3CXFel8iWxAcdSo0yMxLijARC3hg7Io1IVpr30CEnt0CJNZHacoH/IyL1RkO9/3fOVTpbp3NuNnBSFY8xAxhYhXJZBxRcDVJibR8OpkVZbUtJSdn5/v7772fDhg3MnDmTxMREbrzxRoqKiirdLzExcef7cDhMWVlZrccqEpRo1PHKl6u55+1v2LCtmEMzWnDbmX0Z0jM96NBERKQastK9ByI5eQUcp7/lIiJSg3T/L9VV/5sUyH5t3ryZjIwMEhMTWbduHS+++GLQIYkE7ovlmzjv0U/4xYtzKYs6/nhef9742TAl1UREGrAsv6VxbXbVEWnySoFngakBxyEildL9f/2jFmuNwPXXX8+oUaPo168fHTt2ZMSIEUGHJBKYNVt28Oe3vuH1uWuICxljh3bjZyf3omXzxtXdT0SkKercujlxIWNZbkHQoYg0bpcCFwLDA45DRPag+//6x5xrvNN0de7c2a1atWq3dQ1xut36pPz6TZs2jeHDhzNo0KCgQxIBvFkfH//4Ox776DuKSqOcdGg7fn1GH3q0Tdn/ziLSYJnZaudc56DjkN2Fw2EXiURq5djD7/2QhLgQ795wQq0cX0SASUA3QFV9aeB0/39wyq/f4sWLWbNmDWPGjKFNmzYAmFnEOafGWqjFmog0cM45Xp+7hrvf+oY1W4vo2S6F357ZlxMOaRt0aCIiUguy0pOZ8V0e0air1VmORZq0PebmExGRvVFiTUQarLkrt3DHfxfwxfLNtGwez+1n9eWyYzOJD2v4SBGRxiorLZmpizayLr+IjpoZVKT2RNGI3CIiVaDEmog0OOvzi7jn7UW8PHsV4ZBx1eBMfj7iEFonJwQdmoiI1LLMtCQAcnILlFgTqS1XAs8DO1ByTURkP5RYE5EGo6g0wpPTlvG3D5dQWBJhWK90fntmXw5p3yLo0EREpI5kpXszg+bkFTKkZ8DBiDRWA4ACoBhQ/lpEZJ+UWBORes85x1tfr+NPby5k1eYddEtP5jdn9OGkQ9thpvF1RESakqw0L7G2PE8zg4rUmpuDDkBEpOFQYk1E6rX5a7Zyx+QFzFy2iRaJcfzmjD5cOTiLhDj1SxARaYo6t25OOGQsy1ViTURERIKnxJqI1DvOOeavyec/ny7n+VkrMeDSY7py0/cOIS2lWdDhiYhIgOLDITq3bs7yvMKgQxFpvL4G/glcDhwVcCwiIvWcmnzUY6effjqPPPLIHusHDRrEK6+8Uuk+48eP59xzNT+2NDyRqOPznE3c+d8FDLvnQ858eBrPfb6SY7q14b8/HcafzhugpJqIiABed9CcvAKiURd0KCKN02rgIeDLoAMRaVqUA2iY1GKtHhs7dix/+tOfuO6663aumzVrFmvXruWss84KMDKRmlFSFmXG0jzemb+Od+evJ3d7MQCdWjXnB8d147QBGRyZ2VrjqImIyG6y0pL46NuNrN9WRIeWGlldpMYNA1YC7YIORKRpUQ6gYVJirR47++yzufbaa5k3bx4DBw4E4KmnnuLss8/mlFNOIT8/n6KiIk488UQeeughQiE1QJT6r7CkjI+/3cg789czZeF6thWVAdCzXQoXH9WFU/tn0K9jqpJpIiKyV5n+BAbLcguUWBOpDUn+j4jUKeUAGiYl1vZl4sWweVntHLt1N7j0uX0WiY+P54orruCpp57iwQcfpKioiGeffZbp06fTpUsXUlJSiEQinHPOObzwwgtcfPHFtROryEHaWljK+9+s55356/jo240UlUYBGNi5JSP7ZTCyXwY926UEHKWIiDQU3dLLZwYtZEiPgIMRaayWAg7QvzFpKmrz/h+UA2jElFir58aOHcsJJ5zAPffcwyuvvEKfPn3IzMzk5ptvZtq0aTjn2LBhA/3799c/KqlXNmwr4t35XjJtxnd5lEUdIYOjstpwav8MTumXQadWamUgIiIHLjPNa0qTo5lBRWrPIGAw8G7QgYg0LcoBNDxKrO3LfrLJdaFv37707NmTyZMn89RTTzF27Fjuv/9+NmzYwMyZM0lMTOTGG2+kqKgo6FBFWJFXyDvz1/HO/HV8sWIzzkFCOMSwXumc2j+DEX3aawICERE5aJ1bJxEOGTl5SqyJ1JpbgU5BByFSh+rB/T8oB9AQKbHWAJQPYLh48WImTZrE7373OzIyMkhMTGTdunW8+OKLXHDBBUGHKU2Qc45v12/n7a+9ZNqCtfkAJCWEOb1/B0b2z+DE3m1pkRgfcKQiItKYJMSF6NSqOcvzCoMORaTx+lXQAYg0XcoBNCxKrDUAo0eP5uc//zmjR48mJSWF66+/nlGjRtGvXz86duzIiBEjgg5RGiHnHPlFZeRtLyavoCTm1XufW1DCgjX5LPO74bRKimfUEZ05tV8GQ3ulkxgfDvgMRESkMctKT+azZXlEo45QSBPeiIhI46EcQMOixFoD0KJFC7Zv375zuWvXrnz22WeVlh0zZgxjxoypo8ikoSksKfMSY+WJsu0l5BZ4r5sKSsj11+UVFLOpoITSiNvn8Tq2TOTKwZmc2i+Do7u1IS6sWWlERKRuZKUl8fG3G9mwrZiMlolBhyPS+PwVmAhMAVoEHItIE6McQMOixJpII7RyUyHPfraCb9Zt2y2JtqM0ss/9khPCpKU0o0PL5gzo1JK05GakpSSQltKMtOQE731yM9JTEmidnEC8EmkiIhKQrDRvZtBluQVKrInUho3AamArSqyJiOyDEmsijYRzjk+W5DF+eg7vf7N+58QBXmIsgR5tU/zE2K5EWXqKlzhrk+wlzJonqPumiIg0DFnp3sygy/MKGNwjLeBoRBqhO/0fERHZJyXWRBq4guIyXpm9igkzlrNkw3bM4KTe7bhqSBZDe6Zr3BkREWmUMstbrGlmUBEREQmQEmsiDdSy3AL+PSOHl2atYltxGamJcVw9tBtXDs6ia1pS0OGJiIjUqi6tkwgZLM/VzKAitWIj8C6QDfQLOBYRkXpMiTWRBiQadXy0eCMTpucwddFGAHq3b8FVQ7I497COJCXon7SIiDQNCXEhOrVuTo5arInUjmXA5cDdKLEmIrIPugsXaQDyi0p5adYqnv50OctyCwgZnNovg6uGZHFs9zaYqbuniIhUnZn1AiYA6XhDk49xzs2vUGY48BawKGb1YOfcjrqKc3+y0pKZlbMZ55z+LxSpab2Bl/BarImIyF4psSZSjy1ev40JM3J4ZfZqCksitE6K59rhPbj82Ew6tWoedHgiItJwPQ78wzk33sxGAeOBoyopt8g5V29vq7PSkvnf4lw2bCumfapmBhWpUS2BC4IOQkSk/lNiTaSeiUQd7y9cz4QZOXyyJA+Afh1TuWpIFmcP6khivGbuFBGR6jOzdsCRwCn+qpeBR8ysp3NuSXCRHbisdH8Cg9wCJdZEaosD1CBURGSvlFgTqSe2FJbw/OcrefrT5azavIO4kHHmwA6MGZLFEZmt1cVFRERqSvdDNpcAACAASURBVBdgrXOuDMA558xsBdAVqJhY62Fms4EI8C/n3KN1G+q+ZfmT9SzPK+DY7mkBRyPSCA0CkoAZQQciIlJ/NdnEmnMu6BAapPLrputXcxauzWfC9BwmzVlNUWmU9JQEfnZSTy47NlNP30VEJEizgc7Oua1m1hl408xynXMvVCxoZjcCN8Ys10mAmWnlLdY0M6hIrTgaSAg6CJGDp/vX6tH9f9U0ucRaKBTCzCgpKSEursmd/kErKSnBOUc0Gg06lAatLBLl3QXrGT89h8+WbQJgUJdWjBmSyekDOtAsTt09RUSk1qwEOphZnHOuzLwsWFdgRWwh51x+zPtVZvYsMAzYI7HmnLsfuL98ORwO10kNvEub5oTMa7EmIrXgn0EHIHJwdP9/cMrv/5VY27cm980yM9LT08nJyaFXr16EQqGgQ2owotEoOTk55Ofn6x9WNW0rKuWFWav41yfLWLV5BwnhEOcf1okrh2SR3aVV0OGJiEgT4Jzb4HfvvBxv0oILgFUVx1czsw7Aeudc1MxaAGcCT9Z1vPvSLC5Mx1bNWZarxJqIiOyp/P5/5cqVdO/eXff/ByD2/l/2rckl1gA6derE7NmzmTdvXtChNDilpaWsWbMG5xxJSUlBh9NgrN6yg/GfLOO5z1ayrbiMtOQErj+5F5cfm0nbFs2CDk9ERJqeccB4M7sVyAe+D2BmTwCvO+dex0u4XWtmZXh1xheBfwUU7151S09mVs5mnHMaj1Skpr0LTAZ+C7QLOBaRaurUqROLFi3S/X81lJaWsn79enbs2EEoFFIOYC+aZGItHA7Tq1cvJk2axJYtW1QJq6LyLqDRaJQ+ffqQlZUVdEj13tyVW3hi2jLe/GotkaijV7sUfjOsG+dkd9LsniIiEhjn3CJgcCXrr455/wjwSF3GVR2ZaUn8b3EuG7cV005jk4rUrFl4fwWuQok1abDC4TB9+/Zl0aJFvPvuu+raWEXl9//l+ZIRI0aQmKj/ZyvTJBNrAK1ateKCCy5gzZo1lJaWBh1Og9K8eXO6du1KfHx80KHUS5GoY8rC9Tz5v2V8luONnzasVzpjh3bjhEPaKpErIiJSg7J2TmBQoMSaSE37ETAGJdWkUejduzctW7Zk06ZNGjP8AIRCIVq1akXHjh2DDqXearKJNYAWLVrQu3fvoMOQRqKwpIyXvljFU9OWkZNXSHzYGHVEZ8YO7UafDqlBhyciItIolSfWlucVckz3tICjEWlk2gQdgEjNysjIICMjI+gwpJFp0ok1kZqwPr+ICdNzeGbmCrbuKKVVUjzXndiTKwdn6sm5iIhILctK98Z7WaaZQUVqXimQA6QAHYINRUSkvlJiTaSaFqzJ54lpS5k8dw2lEUe39GR+MbI3ow7vTPMEjZ8mIiJSF7q0ScIMliuxJlLzlgG9gV8CdwUciwQmEokEHYIEwMw0i2oVKbEmcgCiUcdH327kiWlL+WRJHgDHdGvDD4d156RD2xEKafw0ERGRutQsLkzHls1ZllsYdCgijU9H4BZgeMBxSCA2bNjAmjVrlFhrwpKSkjjkkEMIh9VwZF+UWBOpgqLSCK9+uZonpy1jyYbtxIWMc7I7cvXQ7gzo3DLo8ERERJq0bunJzF6xGeecJgkSqUkpwJ+DDkKCsGHDBlavXk337t1JTk7W39YmKBqNsnTpUlauXElWVlbQ4dRrSqyJ7EPu9mKenrGc/3y6nLyCElokxjHuhO5cNTiLjq2aBx2eiIiIAJlpSUxbksvGbcUa31REpAasWbOG7t2707KlGhE0VeFwmC5durBgwQJmzpzJ6aefTosWLYIOq15SYk2kEis3FfLIB0t4dc5qSsqidGnTnOtO6suFR3YhpZn+2YiIiNQn3dK9mUFz8gqVWBOpadcBa4GXgw5E6kokEiESiZCcnBx0KBKwhIQEzIz169fz2muvcckll6hbaCWUIRCJUVQa4fGPlvLo1CUUl0U5IrM1Vw/txin9Mghr/DQREZF6KTOtPLFWwNHd2gQcjUgjkwMsDzoICYK6f0r5d6Bt27asXbuW7du3qxVjJZRYE/FNWbCe3/93Pis37aBnuxRuP6sfQ3ulBx2WiIiI7Ee39CQAcnI1M6hIjftv0AGISNDKE2yayKJySqxJk5eTW8Ad/13AB99sIDkhzK9P78NVQ7JIiNPUwiIiIg1B59ZJmMHyPM0MKiIiInVLiTVpsnaURPjbh0v4x8dLKYlEOTe7I786vQ/tNTaLiIhIg5IYH6Zjy+YsU4s1kZq3BPgUOAVoF3AsIiL1kBJr0uQ453j763Xc+cZCVm/ZwaEZLfj92f04pnta0KGJiIhINWWlJzFnxRaccxoXSKQmvQv8BJgCnBxwLCIi9ZASa9KkLNmwnd9Pns//FufSolkcvzurL1ccm0lcWN0+RUREGrLMtGQ+WZLHxu3FtGuh1uciNeZU4FVgYNCBiIjUT0qsSZOwvbiMhz9YzFPTllEacYw6ojO3nHoobVs0Czo0ERERqQHd/JlBl+cVKrEmUpO6+z8iIlKpWk+smVkvYAKQDmwFxjjn5lcoEwLuwXseEgd8AlzrnCsxsyzgO+CrmF0ucM59V9uxS8PnnGPyvLX88Y0FrM8vpn+nVH5/dn+OyGwddGgiIiJSgzLTds0MelRWm4CjEWmEHKBe1iIie6iL/m+PA/9wzh0C3A2Mr6TMWOBw/6cPEAWuj9m+zTmXHfOjpJrs16J127jkn5/ys2e/pKg0yp3n9ue1nwxVUk1ERKQR6pbutVjLydMEBiI1ahPQBm+cNZF6Kisrizlz5gTy2VdffTUffvhhjR5z+PDhdOvWjezsbHr37s0NN9xANBrd735Tp07l7bffrtFYZP9qtcWambUDjsSbQwbgZeARM+vpnFsSU3QQMMU5V+Lv9xZwO3BvbcYnjVN+USkPvreYCTNyiDrHJUd35eaRvWmTnBB0aCIiIlJLurRJwgxy8gqDDkWkcWkJZAM9gg5EJBhlZWXExe09dfLEE0/Uyuc+8MADnHvuueTn55Odnc3gwYO56KKL9rnP1KlT2bJlC6eeemqtxCSVq+0Wa12Atc65MgDnnANWAF0rlPsCONvMUs0sHrgIyIrZnmxmn5vZbDO7zczClX2Ymd1oZqvKf7Zv317jJyT1l3OOl79YxUn3fcRTnyyjf6eWTPrxcdx1/gAl1URERBq6aBQ+vhfWz690c2J8mA6pieTkqsWaSI0KAx8ANwUdiMiB+/zzzznppJM48sgjOeyww3jxxRcBL1k2cuRIjjzySPr168ell15KQYH3/8fUqVPp168fY8eOJTs7m1dffZWsrCxuu+02Bg8eTLdu3bjzzjt3fsbw4cOZNGkSAGPGjGHcuHGcfPLJHHLIIZx//vmUlJQAsG3bNkaPHs2hhx7KsGHDGDduHGPGjNnvOaSmpnLUUUexfPlyAL766iuGDh3K4YcfTt++fXfGMmfOHB577DGeeeYZsrOzueOOOwB45513GDp0KEcccQRHH310jbeuk7rpCloV44G3gY/8n2+BMn/bWqCTc+4oYAQwjL38WXfO3e+c61z+k5KSUuuBS/0wf81WLnxsBje9OJeoc9x9wQBevXYIg7q0Cjo0ERERqQkbv4Gpd8Nzl0LhpkqLZKUnszyvEO9ZroiI1Il+wA0xy3f668r/VC/xl/8WU2YMMDRm+R2/zDsx62K3V8OWLVu45ppreOaZZ5g1axbvvfceN910E6tXryYcDjNx4kRmzZrF119/TcuWLXn44Yd37rtw4UKuvPJK5syZw4UXXrjzeDNmzODzzz/n3nvvZfXq1ZV+7pw5c5g8eTILFy5k/fr1vPzyywDccccdNG/enIULF/Lmm28yffr0Kp3H2rVrmTt3LmeeeSbgdXt9//33mT17Nl988QUvv/wyn376KdnZ2fzoRz/isssuY86cOdx2220sXbqU22+/nTfffJMvvviCiRMncumll1JcXHwwl1YqqO3E2kqgg5nFAZiZ4bVWWxFbyHlud84d5pwbAiwA5vvbip1zG/z3m4Cn8JJrImwtLOW2177mrIenMXvFZq4cnMkHN53A6KO6EgppdFUREZFGo31fOP0e2JwDL/0AImV7FMlMS2Z7cRm520vqPj6RxmwCcEvQQYgcmOnTp7N06VJOO+00srOzGTFiBACLFi3COccDDzzAYYcdxsCBA3njjTd2G6Ote/funHDCCbsd79JLLwUgPT2d7t27s2zZsko/97zzziMpKYlwOMzRRx/Nd995Q8S///77fP/738fMaNGiBaNHj95n/DfccAP9+/ena9eunHbaafTp0weAHTt2cPXVVzNgwACOPfZYli9fvtfx5d5++22WLFnC8ccfT3Z2NqNGjSIUCrFixYpKy0v11OoYa865DWY2G7gcr1XaBcCqCuOrYWaJQHPn3GYzSwd+CfzW39YO2OycKzWzZsD5wJe1GbfUf9Go48UvVnL324vYVFDCkZmt+f05/ejXsWXQoYmIiEhtOfIHsHYufDEe3r8dTrlzt83d0r2ZQZfnFdC2RbO6j0+ksZoMvAL8Ca9rqEisij30f+P/lOtZSZnxFZZHVlJm2sGF5ZyjX79+lbYM+89//sMHH3zARx99RGpqKg899BAffPDBzu2V9X5LTEzc+T4cDlNWtucDngMp57U72rvyMdbmzZvHsGHDOOWUUzjttNO49dZbSU9P58svvyQuLo7zzz+foqKiSo/hnON73/seEydO3OdnycGpi66g44BxZvYtXsLs+wBm9oSZne2XaQlMN7P5wP+Ax5xzk/1tQ4EvzWwuMBtYB/yxDuKWemrJhu1c+PgMbnn5K0Jm3H/RIF780WAl1URERJqC0+6FLsfC9Idh3gu7bcpM82YGXaZx1kRq1kPAGurPQEIiVTBkyBCWLVvGlClTdq6bM2cOJSUlbN68mfT0dFJTU9m2bRvjx4+v9XhOOukkJkyYgHOO7du388ILL+x/J2DgwIH84Q9/4NZbb8U5x+bNm+ncuTNxcXEsWrSI9957b2fZ1NRUtm7dunN55MiRTJkyhXnz5u1c99lnn9XcSQlQyy3WAJxzi4DBlay/Oub9eqDPXvZ/Be/5iDRxZZEoT0xbxv3vfUtpJMqYIVnceMohpCbGBx2aiIiI1JW4BLjo3/CP4fD6TyH9EOiYDUC3dC+xtlwzg4rUrI5BByCyfyNHjiQ+fte94aeffsobb7zBL37xC2666SZKS0vp2rUrkyZN4sorr+S1116jd+/etG3blmHDhu2cHKC23HbbbYwdO5Y+ffqQnp7OoEGDaNWqamOCX3vttTzyyCO88sor/OY3v+GKK65gwoQJ9OjRg5NOOmlnufPOO4+nn36a7Oxszj//fG677TYmTpzIuHHjKCwspKSkhMMOO6zOWrCZ2el4o+6F8PJP9zrnJhzgMXrhdUhPB7YCY5xz8/1tOUAxsMMvfpdz7vmaif4AYmzMg7t27tzZrVq1KugwpAYsWreN/3tpLnNXbaV7ejL3jBrIkVltgg5LREQCZmarnXOdg45DdhcOh10kEqndD1n9BTx1GiS3hWumQkpbikojHPrbtzlzYAceufTw2v18kaZkB97o2e3x+hpJoxaJRJgzZw7Z2dmEw+r7W1NKS0uJRCIkJiZSUFDAyJEj+elPf7rfsdaCVP5dWLx4MWvWrGHMmDG0aePdh5tZxDm318Za/hj7ecBw59w8M8sCvgHaOue2VTUGM/sA+LdzbryZjQJu8Se3LE+sneucq3yQuTqixrxSr5VGojz8/mLOfPh/fLV6K+OO786b1w9TUk1ERKSp63QEnPUg5K+CF6+CSCmJ8WE6tEwkJ09dQUVq1CSgN/De/gqKyN5s3ryZ4447juzsbI444giOO+44LrrooqDDqm0OKG+Wl4qXaCs2s15m9oaZfW5m88zsusp29sfcPxL4j7/qZaCLmfWs7cAPRK13BRWprgVr8rn5pbnMX5NPr3Yp3DNqIId1bR10WCIiIlJfZF8Ka+fBzL/D27+CM+4jKy2Zr1dvxTm334GhRaSKBuGNlt0r6EBEGq527drxxRdfBB1GnXHOOTMbDbxiZgVAa7zJKCPAs8DlzrlvzCwJ+NTMZjrnPq9wmC7AWudcWcwxVwBdgfJJMf/tt477DPilc25j7Z/d7pRYk3qnpCzKIx8u4dEPl+CAn5zYg5+d3ItmcWqGLCIiIhWc8gdY/zV8/k/oMJCs9GxmLM0jr6CE9BTNDCpSI/oCdwUdhIjUMyEzix17637n3P3lC2YWhzc/7PnOuY/N7CjgdWA00A94LuYBWAu8vzQVE2v7c7xzboWZxeON5TYBOL1aZ3MQlFiTemXeqi3c/OI8Fq3fxqEZLbjvwkH076SBHERERGQvwvFw4QRvMoM3buLoQf/gWRJYnlegxJqIiEjtie5nnNtsoKNz7mMA59znfiLuMGCTcy674g5mdiVwo7/4V+ANoIOZxTnnyvyWaV2BFf4xy19LzexB4NsaOrcDojHWpF4oKo1w99vfcN6j0/lu43Z+PqIXr183VEk1ERER2b/kNLj4GbAwpy24mXZsZlmuZgYVqTFRvDYgvwk6EBFpQFbiJcX6APjjovUAXgXyzez75QXNrKeZtXHO/ds5l+3//Ms5twGYDVzuF70AWOWcW2JmyWYWO63qJcCXdXFiFanFmgRu9orN/N9L81iyYTv9O6Vy76hB9OmQGnRYIiIi0pB0GAjn/o3El37AYwkP8PHGQYAmjBWpESFgDruGIBcR2Q/n3HozuwZ4wcyieH9JrvO7bp4JPGhmNwBhIBe4dC+HGgeMN7NbgXygPCHXHnjZzMKAAUuBK2vvjPZOiTUJTFFphL+8u4gnpy0jLhTi5pG9ueb47sSH1ZBSREREqqH/BZSunsvhM/5K0cI/wciJoAkMRGrGmqADEJGGxjn3LN5EBRXXfwecVcVjLAIGV7J+KV630sApsSaB+DxnE//30jyW5RYwqEsr7h01kEPatwg6LBEREWng4r/3O2Z8+jFDtr4Jnz8BR/8w6JBERESkEVNiTepUYUkZ97y9iAkzckgIh7j19EMZO7Q74ZCeJouIiEgNCIV5ov2tdFz3U7q+/UusXV/IOi7oqEQavi+ABXgjHanqLiKyk/rcSZ2Z/l0uIx/8mPHTcziia2veun4Y1xzfQ0k1ERERqVFt22ZwdfGNEG4GL1wJW1YGHZJIw/co3uhFW4MOROqKcy7oECRg5d8BfRf2TYk1qXXbi8v49atfcek/Z7JxWzG3ndmX58cNpnvblKBDExERkUYoKz2Zxa4z3w27Hwpz4fnLoESzhIoclB8Bk4DEoAOR2hYOhwmHwxQUFAQdigSspKQE55wSa/uhrqBSqz7+diO/euUrVm/ZwTHd2nDPqIFkpiUHHZaIiIg0YllpSQDMTR5KzxN+CR/9GSZfD+f/Q5MZiFTXUUEHIHWpY8eOLF26lO7du5OcnIzpb2eTE41GycnJIT8/f+c6fQ8qp8Sa1Ir8olL++N+FPD9rJUkJYf5wTj8uOyaTkLp9ioiISC3LSvce4i3PK4ARt8C6r+CrF6DDQBjy04CjExGp/9q1awfA4sWLlUxpwkpLS1m/fj1bt26lefPmJCerkUxllFiTGretqJSzHp7G8rxChvZM567zB9ClTVLQYYmIiEgTkdnGq/gvyyuEUAjOewyeGAHv3Qbt+0GPkwKOUKQB+gg4F7gXuDrgWKROtGvXjoSEBCZNmrRbqyVpGpxzRKNRAJo1a8bZZ59NQkJCwFHVT0qsSY27661vWJ5XyM0je/Pj4T30hENERETqVPOEMBmpiV6LNYDEVLh4IvzzJHjx+3DNVGjTLcgQRRqedOAwIC3oQKQutWrViksuuYTNmzcTiUSCDkcCEAqFSE1NVWu1fVBiTWrUjO/ymDhzBYO7p3HtCUqqiYiISDAy05JYsDYf55xXH0nvCaOehGcuhOcuhbHvQTNNpCRSZf2AD4IOQoLQrFkzMjIygg5DpN7SrKBSY3aURPjlK/NIjA/x5wsGaDw1ERERCUxWWjLbisrYXFi6a2Wv78HJt8GGBTDpWtAsZyIiInKQlFiTGvOXdxexPK+QX5zSWzN/ioiISKDKJzDIKe8OWm7oDdDvPFj4OvzvvgAiE2nA7gIeDjoIEZH6RYk1qRFfrtjMU58s47Curfj+cRqzRERERIKVleZNnJSTWyGxZgbn/A3a94cP/giL3g4gOpEG6nFgfNBBiIjUL0qsyUErLovwfy/NIy4U4p4LBhJWF1AREREJ2K4Wa4V7bkxIhoufgeat4ZUfwsZv6zg6kQbqfUC5aBGR3SixJgftbx8sYfGG7fz0pJ70at8i6HBEREREyNxbi7VyrbPgwn9BSYE3mUHR1roLTqSh6gG0DToIEZH6RYk1OSgL1uTz6NTv6NMhlR8N7xF0OCIiIiIAJCXE0T61GcsrjrEWq/twOOVOyFsMr1wD0WhdhSfSMOUD3wBlQQciIlJ/KLEm1VYWifJ/L8/FAfeOGkh8WF8nERERqT8y05Ir7woa69hrYdAl8O3bMPVPdRNYbSna6o0bt21d0JFIY3UH0AdYGXQgIiL1hzIhUm3/+N9Svl6dz7jju9O/U8ugwxERERHZTVZaElt3lLK5oGTvhczgzAeg42Hw8b2w4LW6C7AmRaNeq7uP74GP7gk6GmmsvgfcCiQFHYiISP2hxJpUy3cbt/PglMX0aJvMz07uFXQ4IiIiInvYNYHBPrqDAsQ3h9HPQHI7ePVHsO6rOoiuhn18r9fqzkIw9zmNGSe1YyTwR6B90IGIiNQfSqzJAYtGHbe8NI/SSJR7Rg0kMT4cdEgiIiIie8hKq2JiDaBlJxj9NERK4dlLYPvGWo6uBn37Dky9CzIGwKl3Q2mBl1wTERGRWqfEmhywf8/IYdbyzVw1OIsjMtsEHY6IiIhIpXYm1nL3M85aua7HwlkPwtaV8MIVULaPLqT1Rd538PIPoXkrr9XdYZdDYiv47J/gXNDRSWOzBK/V2tNBByIiUn8osSYHZOWmQu55ZxGdWzfn5pG9gw5HREREZK8y07yBoKrUYq3cYZfDsT+BFTPgzZvqd3KqpACevxxKtsGop6B1JiQkeeeQtxiWTg06QmlsDJgOrAo6EBGR+kOJNaky5xy/euUrCksi/Pn8gSQ3iws6JBEREZG9Sm4WR7sWzfY/M2hF37sDepwMs/8NMx+vneAOlnPw2nWwYQGc9FvocdKubUeNBQw+fyKw8KSR6gFsA34VdCAiIvWHEmtSZS/MWsm0JblcfFQXhvZKDzocERERkf3KSksmJ/cAWqwBhOO8FmBpPeGdX8F3H9ROcAdjxt9g/ivQ5ywYesPu29p0h54jYNGbsGVlMPGJiIg0EUqsSZWszy/izjcW0j61Gbee0SfocERERESqJCs9ia07StlSeIDjpTVvBZc8Bwkt4MUxkLukVuKrlmUfw3u3QfohcO7fwWzPMkdfAy4Ks56q+/ikcfsQ+G/QQYiI1B9KrMl+Oef49atfs62ojD+eO4DUxPigQxIRERGpksydM4MeYHdQgPRecOG/oHgbPHsxFG2t4eiqYesqL9EXnwQXT4RmLSov13MEtM7yurOWFddlhNLY3Qj8LOggRETqDyXWZL8mz1vLlIXrOXtQR0b0bR90OCIiIiJVtmtm0APsDlqu58lwyh+9yQBe+gFEIzUY3QEqLYLnr4DCPDjvMS/xtzehEBw5FgpzYf6kuotRGr+7gEeDDkJEpP5QYk32KW97Mbe/Pp82yQn87qy+QYcjIiIickCy0qsxM2hFx17rzbS5ZIrXBTMIzsGbv4A1s2HYL6DPmfvf57DLIS4RPvtH7ccnTcep/o+IiABKrMl+/H7yAjYVlHD72f1IS2kWdDgiIiIiByTzYFusgTeG2Rn3Q5djYcYjMGdiDUV3AL4YD18+7c1WeuKtVdsnqQ0MGAWrZ8GaL2s1PBERkaZKiTXZq/cWrOf1uWsY0ac9Zw3sEHQ4IiIiIgcspVkcbVs0q94Ya7HimsHo/0BqZ5h8PayYWTMBVsXKz+HNm6FVJlzwBITCVd/3qB96r589UTuxSdPzN6AloFytiAigxJrsxdYdpfz61a9okRjHH8/rj1U225SIiIhIA5CVlnRwXUHLpbSFS56FUBw8f7k3kUBt274BXrjC+8yLn/FaoR2IjtnQ+Wj4+iUo3FQ7MUrT0gE4CjiA/K6ISGOmxJpU6k9vLGTDtmJ+e0Zf2qcmBh2OiIiI1CAz62Vm083sWzP73Mz67aOsmdkHZralLmOsSVlpyWwpLGVLYcnBH6zDQG/igIIN8OwlUHKQLeH2JVLqzQC6bS2c/RBkDKjecY7+IZQVeV1JRQ7W+cAUYGDQgYiI1A9KrMkepi3O5flZKxnWK50Lj+wcdDgiIiJS8x4H/uGcOwS4Gxi/j7I3AN/VRVC1JSvdG2dt+cF2By3X9xwY/itYNw9e+7E3sUBtePe3sPwTOOZaGHhR9Y/T9xxISofPnwx2VlMREZH/Z+++46usz/+Pv64sIAkzgISVgAwnggwBRa1abavVOqsVW21FO+yye9ip/Xban92OVlvRaq2zdY+qOAGZLhCBsMLeYWRdvz8+J3CABM5Jzsl9Dnk/H4/b+5x7fO4rAWJy5fp8roOQEmuyh6qdtXz7gTkUFuTys3OP1hRQERGRg4yZ9QRGAZNjh+4H+pnZoEauPRL4GPDz1osw9cpKUtAZdG8nfjMkrN56EF78VerGbTDnX/D6n6HseDj9py0bK68djLwcNlbAe0+nJDxpwzYC1wEPRx2IiEhmUGJN9vCrJ+exbMN2vnnGUPp1K4w6HBEREUm9fkClu9cCuLsDS4D+8ReZWT5wK3A1kNVlTuW7OoOmcNpmTg587M9heub/boC3H0nd2JVz4JEvQcdSuPAOyM1v+ZijrgDLgWm3Yy+gogAAIABJREFUtnwsadvqgOuBR6MOREQkMyixJrtMX7yev7+6mFFlXfnkuPKowxEREZFo/RB4wN3fOdCFZnatmS1r2DxdUyObqWEqaEor1gAKiuDif0JRD3jwalg5t+VjblsfGiPU18JFd0Jxz5aPCdC5Lxx2Jix4BtZl9cxeiVpX4C3gl1EHIiKSGZRYEwB21NTxzfvnkJ+bwy8uGEZOjqaAioiIHKSWAqVmlgehOQGhWm3JXtedBHzRzBYDLwGdzGyxmfXYe0B3v9Hd+zZsmbaURHG7PLoXt0t9Yg2gSz/4+OTQaOCfl8DWNc0fq74O7r8yTNn8yC+h3+jUxQkwelLYT/traseVtiUHOALoEnUgIiKZQYk1AeCmZ99j4ZoqvnraEA7tURx1OCIiIpIm7r4amAFMjB06H1jm7gv2um6Cu5e5ezlwArDZ3cvdvQWZo+iUlxSyeG0aEmsA/cfCR/8fbFoK/7oMapvZffR/P4P3n4URl8HIK1IbI8CAE6H7UJg1Ob3dTOXgt4Esb2kiIpI6SqwJby7fxC0vLuToPp2ZNGFA1OGIiIhI+l0NXG1m84FvA1cAmNltZnZ2pJGlSXn3IjZsq2HTtpr0PGDERBj7BVjyKjz2teQ7hb7zX5jya+g9Aj7ya0hH1Z8ZjJkEOzbB3PtSP760HecBKS6oFBHJVkqstXE1dfV8499zMOAX5w8jL1d/JURERA527j7P3ce5+xB3H+Xuc2PHr3T3fVbhd/fF7p7VE7/K09EZdG8f/AkceirM+Ae8fnPi962ZDw9+FgpLwrpq+e3TF+Owj0NBMUy9Nfnkn0iDS4BrAP0VEhFRYq2t+8vz7/NO5WY+f/KhHNG7U9ThiIiIiKRFWUmaGhjEy82DC/4GJYPhye/A+88d+J6dW+DeS6GmKnQA7dIvffEBtO8Ex1wMq+bC0tfT+yw5eF0F/ATIrOUURUQiocRaG/beqi38/rkFDO5ZzBdOGRR1OCIiIiJpMyDWGbRiXZrXFuvQBS65Bwo6wn2Xw9oFTV/rDg99DtbOD9VuA05Mb2wNGpoYTL21dZ4nIiJyEFNirY2qq3e+8e851NbX88sLhtEuLzfqkERERETSpqxhKmi6GhjE6z4ILrw9VKP982LYvrHx6176LbzzHzjyPBh3TfrjatDzMCifAG8/DFtWtd5z5eDxNHA6MC3qQEREoqfEWht1+8uLmLV0I58+fgAj+neNOhwRERGRtOrYPp/uxQXpnQoab9CpcPoNsO49uP8zUF+35/kFz8JzP4WeR8A5f0hPs4L9GTMJ6mtgxt9b97lycNgAvAasjDoQEZHoKbHWBi3bsI1fPzWPspJCvnb60KjDEREREWkVZSVFLE73VNB4Yz8XuoUueAae/sHu4xsWh2RbQUf4+GQoKGq9mBoMPRM69YHpt0NdmjqlysHrQmAz8NGoAxERiZ4Sa23QbVMWsaOmnh+ffSQdCjQFVERERNqG8pIi1ldVs2l7KyWSzODMG6HfWHj1DzDrbqjeBvdOhO0b4PxboeTQ1ollb7l5MPIK2LIC3n00mhgke6lpgYjILkqstTEbt1Xzr+lLOaK0EycN6RF1OCIiIiKtpjy2zlpFa00HBchrF6rSOveD/3wZ7rkEVs6Fk78DQ85ovTgaM/JTkJMP026LNg7JPrXAf4FXow5ERCR6Sqy1MXe9voRt1XVcdeJArLXX8hARERGJUHmsM2irTgcFKO4BF98NOXmw8HkY8iE48ZutG0NjinvCkR+DxVNg9TtRRyPZ5qPAjVEHISISPSXW2pCdtXXc8cpiSju358xhpVGHIyIiItKqyktCYq2iNTqD7q10GHz8Thh2MZx7M+RkyLfhoyeF/dRbo41DsksecCdwbdSBiIhEL0P+jy6t4eFZK1izZSdXHF9Ofq7+6EVERKRtKesepoIuas2poPEGnQbn3QwdukTz/Mb0GwO9joY598KOzVFHI9lkIjAu6iBERKKn7Eob4e7cNmUhxe3yuHhM/6jDEREREWl1ndrnU1JUQEVrTwXNZGYw5iqo3gqz74k6GhERkayjxFob8cL8NcxftZWLR/ejU/v8qMMRERERiURZSSGLo5gKmsmOugDad4Fpt4J71NFItvgs0BWojzoQEZFoKbHWRtw6ZSG5OcYVJwyIOhQRERGRyJR3L2JdVTWbd9REHUrmKCiEERNh7XxY9ELU0Ui26AeMBrZHHYiISLSUWGsD3lqxiZcXrOOsYaX06dIh6nBEREREIrO7gYGmg+5h1KcBUxMDSdz3gKeAoqgDERGJlhJrbcBtUxYBMGnCwIgjEREREYlWefeQBVgcVQODTFVyaGiuMO8x2Lg06mhERESyhhJrB7nKTdv5z+wVjBtYwlF9OkcdjoiIiEikyktCZ1Cts9aIMZPA6+GN26OORLLBu4SqtRlRByIiEi0l1g5yd7y8mNp656oTVa0mIiIiUlbSULGmqaD7GHQadC2HN/4OtTujjkYy3RLgZ8C0qAMREYmWEmsHsS07arj79SUM6lnMSUN6RB2OiIiISOQ6d8inW1EBFZoKuq+cXBj1Gdi2Ft56KOpoJNONB94BLo06EBGRljGzfDM7tLn3K7F2ELt32lK27Kxl0oQB5ORY1OGIiIiIZISykkKtsdaUERMhrz1MUxMDOYBi4LDYXkSkEWbWzsz+YGbvmdlcM5vcjDGOM7PZZjbfzJ4zsz5x5zw27qzYNqEZ458MVAD/i70fnWycSqwdpGrq6rn95cV0L27HOcP7HPgGERERkTZiQEkRa7dWs2VHTdShZJ7CbnD0BbBsGqyYGXU0kulWA8ujDkJEMtjPAQeGuPvRwNeTudnMcoC7gK+4+xDgMeD/7XXZBHcfHtumNDPGCcA6AHefBoxIZgAl1g5Sj82tZPnG7Vw+voz2+blRhyMiIiKSMRrWWavQOmuNGz0p7KfeFm0ckvkGAZdHHYSIZCIzKwI+A3zP3R3A3VfGzo2OVZ9NN7OZZnZhE8OMBGrd/X+x9zcDHzWz9ikMNdfd39/rWHUyA6Q9sWZmg83slVjZ3jQzO7KRa3LM7Ndm9qaZvWtmfzWzgrjzZ8WOv2dmD5hZp3THnc3cnVunLKR9fg6XHlcWdTgiIiIiGaW8e6wzqKaDNq73cOg7Bt78N2xbH3U0ksm+DJwXdRAikqEOBdYD340l0KaY2alm1gW4BbjU3UcBHwR+Ez/FM05/wjRNANx9C7AZ6B13zbOxqaI3xpJ5ydphZsWEyjrM7GhgezIDtEbF2s3ALbGyvV8AdzRyzWeAY2Pb4UA94cs0sQ/wr8DH3H0wsAK4Lv1hZ6/XFq7nzeWbuWhUP7oWFRz4BhEREZE2pLyhM+haJdaaNGYS1O6AmXdGHYlksp8Cn4s6CBGJSI6ZLYvbrt3rfB5QBrwdS6B9CbgXOAcYCDxuZrOAZ2LXD21GDGXuPpLQTqUH8KtmjPFT4CmgT2xttaeB7yczQFoTa2bWExgFNCz8dj/Qz8wG7XXpMcAz7l4dKxF8HLgsdu7DwEx3fzf2/k/AJemMO9vdOmUhZvCZEwZEHYqIiIhIxtmVWNNU0KYdcQ4Udodpf4X6uqijERGRzFPv7n3jthv3Or+EUDR1F4C7zwQWARuBt+LWRRvu7v3d/Tkz+3ZcI4IzYmPsmoZnZh2BzoSCK9x9SWxfRcgVJd28wN2fIvQ3/hHwCnC8uz+XzBjprljrB1S6ey1ALGm2hFDOF+8N4Gwz62Rm+cBFQHns3B6lf8BioNTM8tIYd9ZasHoLz727mjOO6LVr/RARERER2a1zYT5dC/Op0FTQpuW1g5GXw8YKWPDMAS+XNupm4FTCxCwRkTjuvhZ4FjgDwMwGAAOA6cAAMzut4VozG25mBe7+87hk25OEXFG+mX0gdunVwH/cfYeZdTWzwtj9OcDHgaS77pjZ9919kbv/2d3/5O7vm1nmVKwl4Q7gCeCF2DYfqE12EDO7Nr4UcevWramNMgvcNmURAJNOHBhxJCIiIiKZq6ykiEVrVbG2X6OuAMuBqbdEHYlkqkWEH3s3RB2IiGSozwLfMLO5wEPA1e6+HDiTsPbabDN7m9CZc5/8lLvXAxOBm8xsPnAW8NXY6cOA18xsNjAXKAG+0owYG1spMqnVI9Nd9bWUWHWZu9eamREq0JbEXxSrZPtRbMPMLgbeip1eQljMrkE5cVVwe41zI7Cr/LBv376eqg8kG6zesoMHZixnZFlXRpZ1jTocERERkYw1oHsRs5ZuZOvOWorbaSJEozr3haEfgXf/C+veh5JDo45IMs3/EX4cFhFphLsvBD7QyPEZwCkJjvEqMCzR44mKTTX9EGFttfhprJ2THSutFWvuvhqYQcgwApwPLHP3BfHXmVl7M+sae90d+Dbwy9jpJ4Bjzeyw2PvPA/ekM+5sdeerFVTX1TNpgtZWExEREdmfspJYZ1A1MNi/MVeF/fS/RRuHZCaLOgARkWbbQVjvrR7YFLe9SYZVrEGYA3uHmX2XMPv+CgAzuw14xN0fIWQEnzezekKy7yZ3/w+EdqpmdiXwUGxdtTeBT7VC3FllW3Utd75WQVlJIR88olfU4YiIiIhktAHdw1q0Feu2cVSfpH853XYMOBG6Dw3dQT/wPSgojDoiySQbgCmECVlDIo5FRCQJ7v4C8IKZPeTus1syVtoTa+4+DxjXyPEr416vAg7fzxiPAI+kJcCDxP1vLGPjthq+9sEh5OboV0ciIiIi+1O2qzOoKtb2ywxGXwmPfwPm3gcj9fttibMAOIcwHfRbEcciItIM7j7bzMYAw4H2ccd/l+gYmdK8QFqgrt657aVFdCnM54KR/aIOR0RERCTjDWhIrGkq6IEdczEUFMO0W8Hb1BLGciCDCW3ozo44DhGRZorNrvwL8DPgJOB6GlkXbn8SSqyZ2dUNbUwl8zz99koq1m3jsrFldCjIjTocERERkYzXuTCfLoX5qlhLRPtOIbm2ci4sfT3qaCSTdCEs0tPk3CMRkYz3CWA8oR/A+cBowrprCUu0Yu1EYKGZ/dbMBiUXo6TbrVMWUZCbwyfHlUcdioiIiEjWKCspYvG6bVGHkR1Gx1ZxmXprtHGIiIik1g533wHkmJnFljNLqg12Qok1d78UOAZYBzxrZo+b2UeSDldS7o2K9bxRsYHzju1Dj47tog5HREREJGsMKClkzZadVO2sjTqUzNfzcCifAG8/DFtWRR2NZJITCJOnRESy03YzywdmAb82s68CSU0FTHiNNXdf5e7XE4p9jwQmm9m7ZnZqMg+U1Lr1xUUAXDlhQMSRiIiIiGQXNTBI0phJUF8DT18HW9dEHY1kinJAP4qISPb6HFAAfA3oBBwPTExmgETXWGtvZlea2UzgBuAbQI/Yw/6azAMldSrWVfHk2ys55bCeDOrZMepwRERERLLKgO4hsVah6aCJGXom9B4Bc+6F3x4Jj3wR1syLOiqJ2mRCAwMRkSzk7m+6e5W7r3H3Se5+ATAwmTESrVhbTCjwvcrdj3f3e929zt2nA08nF7akyl9fWoQ7TJqQ1J+5iIiIiABlJaE31yJ1Bk1Mbh585hm48O9QOgxm/AP+OAbuuggWvaiOoSIiklXM7AIz+5qZDY29P8PM3gD+L5lx8hK8boS7VzZ2wt0nJfNASY0NVdX8a/pSjurTibEDu0UdjoiIiEjW2V2xpsRawnLz4MiPhW3J6/DK7+DdR+G9J6HXMBj/RTjyXMjNjzpSaS0vAk8AXyXMaRIRyQJmdiNwDjAN+IyZPQ58BrgO+HMyYyVasfZZMyuJC6C7mf0wmQdJat31egU7auqZNGEgZhZ1OCIiIiJZp0thAZ075LN4raaCNkv/4+Diu+CLb8DoSbBuATwwCW46Bl6+CXZsijpCaQ0vE2o7Fkcch4hIcj4MDHf3i4FTgWuACe7+e3dPqqtRoom1c9x9XcMbd19LyOxJBHbU1HHHKxX06dKBjxxdGnU4IiIiIlmrvKRQzQtaquRQOPPX8NW34JTroL4Wnv4B3HgkPPFd2Lgk6gglnT4NvAsMizoQEZGkbHf3LQCxGZrz3X1ucwZKNLHW2HUFzXmgtNzDs5azdutOrji+nPzchBu7ioiIiMheyrsXsXrLTrZVJ/XLaWlMYTc48evwlblwzp+gSz947Y9w03C47wpY/kbUEUo6HAIMBdpFHYiISFK6mNnZDRtQuNf7hCW6xto8M/sm8BvACG1I300uZkmF+nrn1imL6Nguj4+P7hd1OCIiIiJZrawkrLO2eO02jujdKeJoDhJ57WDEpTD8E/D+s/DqH+GtB8LWfzyMvwaGfBhy9AviSLjDtvWwZQVsroztY9uWynCsbieUDILuQ+K2wSF5urd6YA3hJ8uSfU+LiGSoJYTVIRt778AjiQ6UaGLty4RGytfHHvAi8MlEHyKp88L8NSxYvZWrTxxIx/ZaFFZERESkJQZ0D51BK9ZVKbGWamYw6LSwrXwzJNjm3gf3fAK6HQrjvgDHXAIFha0XU832sDWWIDoY1FbD1pUhObZ5eSxRFpcw27wctqwMibPG5LWHTr0hJw8WPAvzn9jzfGH3kGTrEZdw23Q4jO4L3wJ+nvaPUEQkJdz95FSNlVBizd1XAKeYWVHsvRaiiMgtLy4kL8e4/PjyqEMRERERyXoNFWuLtM5aevU6Cs79M5z6A5h6C0z/Gzx6LTx3PYy+EsZMguKezRu7vh62r4etq0LSaOvqkFzaujp2bFXYb10FOzeHe3oeCUPOCFvf0ZCTm7qPNZ3qamD5DFj3Xly1WVwSrWpN0/cWlkDH3tBjKHQshU59oFNpONapNBzr0DUkRBuetaEC1s7fc1v1Fix5Zfe41UUw5nrYOA/uW727uq37kFD11pqJUxGRCJi7J3ahWT4wAGjfcMzd56QprpTo27evL1u2LOowUubN5Zs46/cvce6IPvz248OjDkdERCRyZrbc3ftGHYfsKTc31+vq6qIOIyEbqqoZ8dOn+fiofvziAq2+3mp2boVZd4c12DYshtx2MOwiGHcN9DwsXFOzPZYQWx1LmMUlyHYdWw1Vq0PDhKZ06ArFh4TEXXEvwOH9/8G2tbHz3UJV3ZAzYNCp4fpMUV8Pq9+ChS/Aoheg4hWo3rrnNTn5sURZ730TZZ16h61jaZiimwruIYHXkGhb05B0ew82NdKoonP/uAq3hoTbYCjqoenAIlnMzOrcPdFZkAe1hD4JZnYWcCvQFaiK7SsIiTZpJbdOWQjAlRP0aRcRERFJhS6F+XRqn6eKtdbWrhiOuwpGfwbefRRe/QPMvDNsXQeENcB2bmr6/tyCkCzrVAq9h8cSZ4dAx0N2v25IpjWWUKqvhxUzwlTH+U/C3H+FzXKh/9hYNduHQhKooYKrtaxfFJJoC1+ARS/uTgDm5IfquoEnQekxsYRZ71CJ1poJKrNYkrInlJ+w57nqKlj3/l5Vbu/Boimw4Jk9r80tiKuciyUAd72O7Yt7Zk81oYi0WQlVrJnZTOBjwEPuPsLMJgLHuPs30h1gSxxMFWvLN27nxF/+j3EDS5h85XFRhyMiIpIR2nrFmpl9FHjB3Teb2deBscCP3P3NKOPKpoo1gLP/8BKrNu/g9e+eFnUobdvSqWEdtjXv7q4uK+4ZS5j12vNY/JTFVNi8At57KiTZFj4PNdvC8S5lIcE25IyQREpV1Ve8rWtCIq0hmbaxYve5XkfDwJNhwMlQNg4KilL//FT4FrACuLOJ8/V1sHFJSLKtnQ/rFuxumLB5eZjK2xjL3bPybo8kXFw1Xq7WnhZpbapY2y3RT0K9u1eYWR6Au082s68e6CZJndtfWkRdvataTUREROLd4O7DzOwYYCLw59g2Idqwskt5SRFzlm1iW3UthQX6GSEy/caELQqdesPIy8NWswMWv7S7mm3qzWHLL4JDPwCDTw9bp9LmPWvnljClc+ELIYm3+q3d57oNhJFXhKq08hOhKEvabM4A3tvP+Zxc6DYgbENO3/d8zfa4RFss2barU+mKkGxcNo3QR29vsQq6+MRbx1Jo1zE0YcjND9VxDa9z8mPH4l7n5IVrdr1u7J6CzJ666h6mRNdVx7aaA7yuCR9fXrvdH+8+W37sfEF2VQ7W18c+xp2hoUfdTqjdGY7tvd/nWNw99XWQ3wHyC0NSO78wrBmYXxTbF+55LFf//8hGZnY7+35x2Qi86u73JTJGon/yNbH9MjM7F1hMmA4qrWDzjhrumbaUoYd05KQhPaIOR7KBO8z+Z1h3ZMgZ0OOw1p/GICIiraFhYanTgVvc/WYzuzrKgLJReUlDZ9BtHF6qzqBtXn57GHxa2D7yq1BBN/8JmP8UzHsM3v1vuK50+O4GCKUjmk661O4MSaGGddKWv7F7TbiinnD0hTDgpJBM69K/dT7GVHsSaEnOKb8DlBwatqbs6ni6V+It/nXlHPA0VstaTlwyLjdU1FlO7HjD69zwffcex3LirrUmjueEv0MNr7F9E2H7S5TV1xww/BZ/7LkFYT3EXYm4uMRbY8k5s/BzidcDsX2j7xO5puF93PX1tXsmwupqwr+3dH8umpJbEJeESzAhh+3+uPbY08Txpvb1ex0j7LsNCM1hZH92AuOBewmf+YuAWcCXzGyEu3/3QAMkmli7ycy6At8H7gG6AF9uVsiStHumLmHrzlqunDAAU3JEDqSuFv77lbBGCcAzPwzfpO2axjAhPdMYREQkCrlmdhxwPnBF7JjmRCWpvHuYXlexrkqJNdmTGfQ8PGwnfDWs/bbgWXjvSXjvaXjhF2Er6hmq2IacERJk8eukLXl199TSgo4w6IPhmoEnHzy//GyNQq68gvA97f6Sj/V14RfLm1eEz3l9TSzxVBN7XRvbxxJS9bVxianaPa9rSFY1dU99bSyZEbfV18W9r4tLANVBfXXc8VgSZJ/rG8aJJZH2rh7LLQiVeHscy28ksdXY672O5eTt+fHXVu+VrNu5+3Xtzr2OxyX2aqtDxeGOzfveg+9OEjYkFfd4z56JxEavsdi/kUbO5+SF5FRuLLmXF0v87bNvOB+/bzhf0Mix2N5yoXZHWDuwZhtUb4Oaqth+W+PH4q/dsTF06224JgrlE5RYO7DDgfHuvhXAzH4HPAacAUwHWp5YM7NcoNrdNwBvAINbErEkp6aunttfXkyPju04e3jvqMORTFddBfddEb7ZG3QaHPfZ8E3f/MdDa/upt+yexjDkQ+EbwI6HRB21iIg03/eBm4Fn3P0dMxsKzI84pqxTVhISa4vWbos4Esl4hd1g2IVhq6uFZVPDdNH5T8KsyWGLl1sA/Y4LibQBJ0PvEQfndLElwEzgeKB7hHHk5IYpus2dpiuSLu4hARmffKvZFuqjjPCfXQnEuP2uJGMj53btafqevPYRfLBZp0dDUg3A3beaWXd332ZmOxMZ4IBf1d29zsy+B9zfgkClmR6dU0nlph1844yhtMvLonnt0vqq1sLdF4XpBcMvhY/eFH4bNfiD8OFfwJp5u9cKiZ/G0PvYkGQb+iHoNezg+K1pIjavgLcfCd+AdSmDrmXQuV+sJFtEJDu4+3+A/8S9n0eoXpMkDIirWBNJWG4elI0P2wd/DBsqQgOExVNCZ9OBJ0G/sW3je4tHgc8DTwPqASKyL7PwtaCgEIqizD5LI+aY2d+A22PvPwXMNbN2QEJzyxPtCnobcIe7v9TcSKOQ7V1B3Z0zf/cSi9dV8cq3T6FLYUHUIUmmWr8IJp8P69+HCV+HU76//wTZtvWh5fn8J+C9Z3a3s+9Yuru9/ICTDr5vBGurQ/XezMnh4/f6fa8p6hmSbF367064Nbzu3C+UhYtIxlBXUPsJcCOwCfgvcBxwtbtH+gvRbOsK6u4M+/FTHFHaiXuvHhd1OCLZZz7wGiGppkk2Im3CwdIV1MyKgR8Ap8YOPQf8BNgOdHX3NQcaI9FPwljgcjNbCMSXyB2bVMSSlFfeX8fblZu5fHy5kmrStBUz4a4LQ8Xamb+B0Vce+J7CbjDsorDV1cDS12He46Ga7Y07wpbXHgacuHttts5Z/HPrqrdCMm3OvbBtXVgvYfAZMPwSaNcpdJrauCT8prnh9bJpjQxkodNUl1iyrWvZnq879UlNxyT3sD5FU+s5VFfFSsmrwp9ffoc9F0NtWCQ1v0PcgqlF2dXNSUQSdY67/8DMPkhoZHA8YT1czTRIgpkxoHsRFes0FVSkWYbENhGRLBObBvrNJk4fMKkGiSfWvpDgdZJCt05ZSI7Bp48fEHUokqkWPAv/+mRYePTjd8LhH01+jNx8KD8hbGfcAOvej60V8gS8/1yY0vAocMjRu6vZ+hyb+Uma7RvhzX+HhNqKmeFYyWAY/yU45mLo2Gv/99dsh41LQ6Jtw+KQbNtYEZJva96FJa/se09OXkiu7Uq4lUG74r0SZAksdFqzLT0drXLb7dWRqImEXHy3ovzC9LeW36ODUhOJwdaIY2+71sLYHvdn1cifWe2O1o0rW428IvO/bmSnhtLbk4D73H2emR14OoLso6ykiDnLNrG9uo4OBfq7KiIi0haYWR5hGY1DicuRuftPEh0jocSau7+QdHTSIvNWbuH5eWs48+hS+pccZNPxMkF9fSzBsb2J5EZV6Awz5EMh8ZSJZv0THrkGCoph4v3Qf2xqxi05FMZ9Pmw7NoXk2vwnQ4Jtyq/DVtg9ND4YfBr0HR2mSGbC2mz19bD4xZBMe+c/IeFRUAwjLgtbvzGJx5nfAXoMCVtjdmyOJduW7FvxtnwmLHpx/+Nb7p6JrIJiKO6ZeIvuhmtyC/ZMyCWTxNu2AWqW774u0+V1aLpV+f4+V5bTyOehYd/U14CGz4nyEylz7KcAJSvSoMrMvgVcDBxvoX24ytyboTz2/VbF+ioO66XOoCJJ2QgMA84Fboo4FhGR5NwD9AKmkuCaantLKLFmZv+jkZ8u3P2U5jxUDuy2KQsBuHKCqtUatXkhCvrSAAAgAElEQVRFSFzsd6pc7Ifmxq6p3Z7Yc3ocBh/+ZVh8NlO4w0u/hWd/HBJaE++HHkPT86z2neHIc8NWXxcaIzRMGZ19d9ggrEvWZ2TY+o4MDRE6dElPTI3ZUAGz7g7bpiXhWP/xMGIiHHFOqBpLtfadoNdRYdubO2zfEJJsNdsbrwbLLciMZGSD+vrw76KxNuIJrMXZfB6ms8YnteP/vdY0ElPDNVtX746xuVVjuQV7Vu617xym++5duddU0i6/EPLbE2vnJPtjSqqlyeXANcA33X2VmQ0CJu//FmlMeawz6OK125RYE0lWR+AQou0IKiLSPEcDh3kiDQiakOhU0F/HvW4PfAK1ck+b1Zt38PCsFYwu78qI/l2jDiczPXwNvP/s/q/Ja79nRUthNyjo18gPx01Mh1v9Nrx8E/zj7JBYOv366NcZq6+Dx78F026FQ46CS+8LSYDWkJMbKr76jYHTfhgqtBa/BMumh4TbgqdDY4AGJYOgz6jdybZDjgpVgKlSsx3e+S/MvBMWxYpqO5bCCdeGrqjdB6XuWckyC3/fCrtFF0OycnLC3/2CIqBH1NEkr76u8SR7zbZwbu9pprsSnBlakSqSIHdfAHzFzHqbWe/Y+59HHVc2Ku8eKtYWqzOoSPJygcaWpxURyXxLCdX+O5s7QKJTQR+Nf29mDxM6JUga3PfGMqrr6rlywsCoQ8lM7iGZc8jRcMb1e00Hi0uQtXQtnyM/BsM/AU9+D956MFRpnfgNGPeF1CaIElWzHR6YFKY4lk+Ai+8K1TVR6dI/fH6GfyIW3w5YOSck2RqSbXPuCRuEyqBeR8cl20ZBt4HJVW25h/XSZk6Guf8O3Uxz8kNV2ojLYOAHIDfrG9NIc+TkQruOYRNpQ8zscODfQGl4a8uAC9x9XrSRZZ+GirUKJdZERETakgXA82b2ILBrGoy7/y7RAZr7E2guaqScNm+v2IwZnDQkC6tGWsOGRSGhUn48DDw5vc/qWh4SWO89A49/M0y/nDk5TA8dfFp6nx1v+wb45yWw5FU46nz42J+jSe7tT3773RVtDbatDwm2+GTb8jd2n2/fZfcU0oZkW1Ejcwiq1sKcf4XP/eq3wrGeR8Kx34GjL4KikvR+bCIimetPwA3ufjeAmV0M/AX4QKRRZaFuRQV0bJfHorVKrIk0y7+Ad4AfRh2IiEhS2gHvAofHHUtqWmiia6w9GDdwLmFpyseSeZAk7v01W+nduQPt87UeTaMqZ4d96fDWe+bg02DAq/DqH+HFX8Fd58NhZ4Uuml3L0/vsjUvhrgtCJ8px18AHf9r63RGbq7AbDP5g2CBUnG1YvDvBtvwNqHh5z2m9XfrvrmrrVApvPRTWdauvgXadYfSVYe200uGZtUaZiEg0ujYk1QDc/R4z+3aUAWUrM2PwIcW8uXwz26prKSxQBbRIUu4D7ge+j3rViEjWcPcrWjpGot8xPBT3uhb4mbu/3tKHy77q653F66oYXZ5FazO1thWzwr70mNZ9bl47mHAtDLsInvp+mB664Bk44atw/JfDWm2ptuotmHwBbFkBp98A469J/TNakxl0GxC2oy8Ix+pqwse5fDosnxGSbW89CG89sPu+gSeHqZ6HnZmez7OISPaqM7Mj3P1tADM7ggQ6WpnZYODvhKXGNwGXu/tbe10zDvhz7G0+8BLwJXdv9hokme7cY/ty3UNv8vCsFVwypn/U4Yhkl18CP0P9fEQkK5jZSe7+gpmd3dh5d38k0bESXWPt74kOKC1TuXkHO2rqObRHGroYHiwqZ0FeB+g+JJrnd+4LF94BI68I00Of/7/QjfJDP4ehH05dFdWiKXDPJ8Laauf/dXci6mCTmw+9h4dtdOzYjk0hgbphUVg3rWtZpCGKiGSw7wIvmtmc2PujgS8ncN/NwC3ufoeZXQDcwe6vwg1mA6PdvcbMcgi1KJ8HfpuSyDPQuSP68PPH3uHOVyu4eHQ/TJXRIokbEHUAIiJJmQi8AHy1kXMOJJxYS2g+mZk9ZmYlce+7m9l/E32IJG7hmq0ADOheFHEkGco9TAXtdXT0i9QPPAk++1KoJNu2Hu65BO66ENa93/Kx37wfJp8XPt6J9x+8SbWmtO8cPr8jL1dSTURkP9z9ScKaIDfGtiM4QFdQM+sJjAImxw7dD/Qzsz3aKbv7Nnevib0tADqQ5Joj2aa4XR7nHduXtys3M2PJxqjDEckuNcAKQMsUikgWcPdJsf0HGtlOSWasRBdq6u3u6+ICWIuaF6TFwjXh/0QDeyix1qiNS8JC/q09DbQpuflheuYXp8Owi2HB0/CnsfDsT6C6md9VvPon+PenoUM3+PTjIcEkIiLSBHdf4+7/jW1rOPBErH5ApbvXxu53YAmwz9xHMys3s9nAWsKU0T+lNvrMM3Fs+IXO5NcqIo5EJMvcD/RBK3GLSNYxs1IzG29mJzZsydyfaGIt18x2lQeZWQHhN5eSYg2dqAZqKmjjGhoX9G7FxgWJ6NgLzrsZrngiTFGd8hv4w+iwVpgn+Mv9+vqwdtuT3wljXPl0qMwTERFJTsqqytx9sbsfA/QidM06r7HrzOxaM1vWsHmi/+/LQEN7dWTMgG48OqeSdVsP2uXkRFLvKOAaoDziOEREkmBm3yP0NP4D8JvY9utkxkg0sfY4cJ+ZnWxmJwP3ot9FpMX7a7bSPj+H0k7tow4lM1U2NC7IsMRag7JxcNUL8JFfQ/VWuO9y+Mc5sGbe/u+rrYYHr4JXfg/9joNPPxm6Y4qIiDTCzIY1tREaDezPUqC04ZemFhYS60+oWmuUu28F7gEubeL8je7et2HL9rXJLhtbRnVdPfdOXxp1KCLZ4yjg9+y7WqOISGb7NHCoux/r7qNj25hkBkg0sfY9YBah18svgTdixyTFFq6porykiJyc7P6GNG1WzILcdtBjaNSRNC03D8ZMgmveCJ0sF70Afx4fqtF2btn3+h2b4a4LYO59cNhZ8MmHoVBdYUVEZL8e3s+2fX83uvtqYAZh0V6A84Fl7r4g/jozG2Rm+bHXBcC5wBzagDOO7EX34nbc9doS6uqzt/pOREREDmhV/NJnzZFQYs3da9z9x+4+JrZdH7eYraTIjpo6Vmzaro6gTdnVuOCosLZZpivuAef8Aa58NkzpfOX38PtRMOdfu6eHbq6E2z8Skm+jPg0X/QPyO0Qbt4iIZDx3H7CfbWACQ1wNXG1m84FvA1cAmNltcW3nTwFmxtZYmwmsAn6ahg8n4xTk5XDJmH4s37id5+etjjockezgwEXAT6IOREQkKU+b2f8zszF7zQBIWKJdQW9rpCvozclGK/u3eF0V7mpc0KTNy2Hb2sxpXJCovqNCcu2jN0FdNTwwKSTT3vkv/PV0WDUXTvk+nHkj5ORGHa2IiLQB7j7P3ce5+xB3H+Xuc2PHr3T3R2Kvb3H3o9z9GHc/0t2/5O47oo289XziuP7k5hh3qomBSGIMeJYwt0lEJHt8EjiHsORZQ/X/Q8kMkHfgSwAYuXdXUDPT7PkUa+gIOqC7EmuNamhckKnrq+1PTi6MvBwOPxueux6m/w2WvAKWC+f8EUZMPOAQIiIi0npKO3fgtMN78tTbq6hYV0VZib4/Ezmg1YB+TywiWcLMcoFvuPu/WzJOomus7ZGAiy1yq66gKaaOoAewIta4INM6giajsBucdSNc9TwcfSFc+i8l1URERDLUZWPLcYe7X2+yr4OIxFNSTUSyiLvXAd9t6TiJJtZeM7M/mFmZmZUT2pC+2tKHy57eX7MVUMVakypnQW4B9Dg86kharvdwOP82GHRa1JGIiIhIE8YfWsLA7kXcO30pO2rqog5HJPO9DTwI1EcdiIhIwmaY2QktGSDRxNrXgCJgGvA6oVrthZY8WPa1cE0V3YsL6NwhCxbmj0LlbOh5BOSpWFJERETSLyfHuHRsGRu31fDonMqowxHJfL8FzgM2Rh2IiEjCxgLPm9l8M5vRsCUzQEJrrLn7ZuAKMzsM+AxwGTACmJxsxNI4d2fhmq0c1qtT1KFkps2VsHUVDPlQ1JGIiIhIG3LBsX351ZPvcudrFZw/sm/U4YhktsuBk4H20YYhIpKEL7R0gAMm1sysEPg4IaE2EOgAjHP3d1v6cNltfVU1m3fUqiNoUxoaF2Tz+moiIiKSdToX5nPOMX24d/pS5i7bxNF9O0cdkkjmOj62iYhkCXdv8WzM/U4FNbNbgaXA2cAvgP7ARiXVUm/hWnUE3a/KWOOCbOwIKiIiIlntsnFlAEx+rSLiSERERCSVzKyDmX3TzO42swcatmTGONAaaxcDc4Cbgf+6ey3gzYxX9mNhrHGBOoI2YcUsyMkLa6yJiIiItKKj+nRmeL8uPDx7OZu21UQdjkjmehXoB9wRcRwiIom7FSgHxgP/A8qApH6TdqDEWilhHbUfABVmdj2glfXToKFirVWmgm7fAI99AzYuTf+zUqVyNvQ8HPK1YIOIiIi0vsvGlrGjpp773sii759EWlsnwk+QHaIOREQkYce4++eBze7+e8JKkSOTGWC/iTV33+ruf3X38cCHCMtQFpjZK2b2+WYGLY1YuKaK3ByjX9fC9D/suRtg6i0w4+/pf1YqbF0NW1ZA6TFRRyIiIiJt1JnDSulamM9dry+hvl4TOEQadSQwlbBCt4hIdtge29eaWZG7bwF6JDPAgSrWdnH3t93960Af4DfAmck8SPZv4Zqt9O9WSEFewn8kzbNmPkz/W3hd8Wp6n5UqDY0LtL6aiIiIRKR9fi4Xje7HorVVvPz+2qjDERERkdRYb2ZdgceAJ83sIWBZMgMkncVx91p3v9/dlVhLkdq6epas38bA1mhc8PR14HXQpT8smwa1O9P/zJZaEWtc0HtEtHGIiIhIm3bpmDLM4M5X1cRApEl/AP4adRAiIgk70903ANcBfwaeAc5PZoA0l0dJIpZt2E5Nnae/I+jCF2D+E3DU+TDiMqjbCStmpveZqVA5CywXDjky6khERESkDetfUsjJQ3rwzDurWLFx+4FvEGmLfgP8MeogREQS4+51ZlYKnOTudxGad+5IZgwl1jLAwrWt0BG0vg6e+h7ktoNTfwhl48PxipfT98xUqZwNPQ6DfK2CKiIiItG6bFwZ9Q7/nLok6lBEMtMjwANRByEikhgzuwB4jd39jI8AHkpmDCXWMsDCNa3QEXT2P2HlXBj7OehaBn1GQk5+5q+zVrUONi2F3lpfTURERKJ30pCe9O3agX9OXUp1bX3U4YhknqOB8qiDEJFMYmZXmJmb2ceace/gWAPN+WY2zcyOjDu32Mzmmdms2Nac1infAY4FNgC4+2ygLJkBlFjLAAvXpjmxVl0Fz/4UCktgwrXhWH6HkFxb+nqoZstUlbH11dQRVERERDJAbo5x6XFlrN26kyffWhl1OCKZpxpYDtRGHYiIZAIzKwcmEarCmuNm4BZ3HwL8gt2VZQ0+7u7DY9u9zRi/zt3X7XWsOpkBlFjLAAvXbKW4XR49itul5wEv/w62roSTvwPtO+8+XjYOdm6GVW+m57mpsCuxpoo1ERERyQwXjepLQW4Od76mJgYi+/ge0BfQbGmRNs/McoDbgC8CO+OODzazR2MVaHPM7Jom7u8JjAImxw7dD/Qzs0EpDHOLmR0CeOyZpwLrkxlAibUMsGhtFQN7FGFmqR98cyW88jvoPhRGXrHnubLjw77ildQ/N1VWzALLgV5HRR2JiIiICAAlxe04c1gpUxetZ97KLVGHI5JZTiT8CN0+6kBEJANcC7zs7m80HDCzXOCfwNfcfTQwFrjKzEY3cn8/oNLdawHc3Qlp+/5x1/zDzOaa2V/NrEczYvwW8Dgw0MxeAv4BfC2ZAZRYi9jWnbWs2ryTgenqCPrc9VCzDU7/KeTm7Xmu3xjAMjuxVjkbug+BgjR3TBURERFJwsSxYfmVyapaE9nTR4HfAb2jDkRE0izHzJbFbdfGnzSzo4Dzgev3um8ocCRwj5nNAl4BOhKaBiTrRHcfRlgjbS3w92QHcPfpwAeAS4CfxWJbncwYeQe+RPax8k3YshIGn9bioRbFGhcM6J6GjqCVc2DWXTDwZBh8+r7n23eGXkfDklfBHdJRMdcS29bDxgoYdnHUkYiIiIjs4dj+XTiitBMPzFjGtz58GMXt9G21iIi0KfXu3nc/5ycQWpm8F5ud1wu4BfgxsN7d91nvycw+SahyA7gJeBQoNbM8d6+1MFB/YpPN3b1hX2Nm/w+Y35wPxN03EarWGuKYw55VcfulirVk1dXAPy+Gf18B6xe2eLiFa7cCaWhc4A5PfS+8Pv36ppNmZeOhag2sW5Da56fCyjlhr8YFIiIikmHMjE+OK6Oquo4HZyyLOhyRzLEYuAC4J+I4RCRS7v5ndy9193J3Lyc0L7iK0Ixgs5ntWqvKzAaZWTd3/0dcI4Lb3X01MAOYGLv0fGCZuy8wsyIz6xL3yEuAmSkKP6mqIyXWkpWbDx+9CXZugX99Cmp2tGi4hWvS1BF0/hOw6EUYcWmoSmtK2fiwz8TpoCtijQt6q3GBiIiIZJ6zh/emY/s87nytgrDsi4hQDzwAvB11ICKSiWLrpZ0FnBdrXPAW8FegQxO3XA1cbWbzgW8DDQm5Q4D/xcaYC5wEfDJVYSZzsWrWm2PQqXDSN+GFX8CT34GzftvsoRaubZgKmsLEWl0NPHUd5BfBB76//2v7jwv7ildg5KdSF0MqVM4CbP+JQREREZGIFBbkccHIvtz+8mKmLlrPcQNLog5JJHrlQA2QG3EcIpJR3P3kuNfvE1ZkTOS+ecC4Ro4vBEY0Nx4zG7af0/nJjKXEWnOd9C1Y8hpM/xv0Hw/DLmzWMAvXbKW0c3sKC1L4R/HGHbDuPTj5u9CpdP/XFveEksGwJAMr1ipnQ8kgaNcx6khEREREGjVxbBm3v7yYO1+rUGJNBDQnSkSyxcP7Obc9mYH0Za+5cnLh/NuguBf858uwZl7SQ7g7i9ZWpXYa6PaN8L+fQcfeMP6axO4pGwcbl8CmDFofZMemsIadpoGKiIhIBju0RzHHDyrhiTdXsnpLy5YIETloTAWeizoIEZGmufuA/WwDkxlLibWWKO4JF/wNareH9daqq5K6fdXmnWyrrkvtNNApv4Ht6+HU66AgwXH7N6yz9mrq4mipSjUuEBERkexw2dgyauude6cujToUkczwWeAzUQchItI6lFhrqfLj4ZTrYM078OjXQjfOBC1cE+sI2r04NbFsWAyv/wV6DYNhFyd+364GBi+nJo5UqIw1LihVxZqIiIhkttMOP4RDOrXj7qlLqK2rjzockehdB/wq6iBERFqHEmupcPxXYPDpMPufMHNywrc1NC5I2VTQZ34EddVwxg2Qk8QfbZf+0KkPLMmgirWGjqCl+1tPUERERCR6ebk5fGJMGZWbdvDsu6ujDkckeucCF0QdhIhI61BiLRVycuDcm6FzP3js67BybkK3LVwTEmuH9khBxdrSqfDWgzD0IzDgxOTuNQtVa2vehap1LY8lFSpnQ7eB0L5z1JGIiIiIHNDFY/qRl2NMfq0i6lBERESkFaU9sWZmg83sFTObb2bTzOzIRq7JMbMbzextM5tjZv8zs0Gxc+VmVmdms+K2Q9Mdd9IKu8GFd0B9XVhvbcfmA96ycO1WCvJy6N2lQ8ue7Q5Pfhdy8uCDP2neGP1j3WszoWpt5xZYt0DTQEVERCRrHNKpPWcc2Ysp763dtdyHSJt1G9AHmBV1ICIi6dcaFWs3A7e4+xDgF8AdjVxzNnA8cIy7DwOeBX4Wd36Luw+P295Pd9DN0ncUnP5TWP8+PPLFA663tmhtFeUlheTmWMue+9YDsGwajPoMdB/cvDHKjg/7TEisVc4BXI0LREREJKtcNq4MgLteXxJxJCIR6wT0BbTkoIi0AWlNrJlZT2AU0LDw2P1Av4ZqtDgOtAPam5kRvhQvS2dsaXPcZ+Hws+Hth2DabU1etrO2jqXrt7W8cUHNDnj6R9CuM5z0reaP02ModOiWGQ0MKmeHfW9VrImIiEj2OG5ANwb3LOa+6UvZXl0XdTgi0bkIeB04NupARETSL90Va/2ASnevBXB3B5YA/fe67j/A88BKoBI4FfhB3Pmi2DTSGWb2AzPLbexhZnatmS1r2LZujaAM3wzO+QN0HQBPfAeWv9HoZUvWbaPeYUBLGxe8/hfYtARO+gYUlTR/nIZ11irnhKmYUWroCNpLjQtEREQke5gZl40rY/OOWh6ZvTzqcERERKQVZErzglHAUYSZ+L0JU0H/EjtXCfRx99HAacAE4GuNDeLuN7p734atuDgFTQGao31nuOjvYDnwr8th+4Z9LtnVEbR7CxJrVWthym+gazmMuar54zToPw68LjRCiFLlbOhSFtatExEREcki547oQ2FBLv94tQI/wLIgIgetzcAvgcejDkREJP3SnVhbCpSaWR5AbJpnf0LVWrxPAs+5+0Z3rwf+DnwAwN13uvvq2Ov1wN8IybXMVnoMfPgXoZrswc/ts95aQ0fQgS3pCPr8/8HOzXDajyGvXUuiDcrGh32U66xVV8Ha+ZoGKiIiIlmpY/t8zh3Rh7dWbGbW0o1RhyMSjVrgW8ADUQciIpJ+aU2sxRJiM4CJsUPnA8vcfcFely4ETjGzgtj7s4A3IazTZmb5sdftgPOAmemMO2VGXg5HXwTzH4dXfr/HqYZuUc2uWFszD6bfDv3GwhHntDDQmF7DoKAYKl5JzXjNsXIueL0aF4iIiEjWmjg2NDG487WKiCMRiUgXwhprP4k6EBGR9GuNqaBXA1eb2Xzg28AVAGZ2m5mdHbvmj8AiYLaZzSGssfa52LkTgJlmNpuQpFsJ3NAKcbecGZz1W+g+FJ75EVTsrgRbuLaKroX5dC0qaPr+/XnqujBt84wbwnNSITcP+o2BZdOhdmdqxkxWQ+OCUlWsiYiISHY6vLQTo8u78t85layvqo46HJHWlwOMAUqjDkREJP3Snlhz93nuPs7dh7j7KHefGzt+pbs/Enu9090nufvh7j7M3U9394Wxcw+4+1Hufoy7H+nuX3T3iLI+zdCuGC76R5iq+e8rwrpowKK1Vc2fBrrweXjvSTjqAug7KnWxAvQfD3U7YfmM1I6bqBWxxgVKrImIiEgWmzi2jOraeu6bvjTqUESisYNQEiEicpDLlOYFB7eeh4XKtS2V8MAkNm7dzvqqagY0ZxpofR08+X3IbQen/TD1sZaNC/slEU0HrZwNnfu1rMOpiIiISMQ+dFQvuhcXMPn1Curr1cRA2qCPAIdHHYSISPopsdZajrkYjv0UvP8cVc/+EoCBPZqRWJt1N6yaC2M/B136pzhIoM9IyC2IZp21mu2w5l2tryYiIiJZr11eLh8f3Y+l67fzwntrog5HpPWdC1wOKK8sIgc5JdZa04d/AYccTe+Zv2V8zpsM7J7kVNCdW+G566GwO0y4Nj0x5neA3sfC0qmhOq41rXwzrBunaaAiIiJyEPjEcWXkGEx+VU0MpA36IvBbIEXLQYuIZCol1lpTfge46O9U53bgpvw/MLhwS3L3v/J72LoSPvAdaN85PTEClI2HnZth1Zvpe0ZjKmPrq/VWYk1ERESyX58uHTj18EN4bt5qlq7fFnU4IiIikgZKrLW2kkP5e/dv0MM2M+D5L0FdbWL3bV4BL98UOowee3laQ6RsfNi39nTQhsSapoKKiIjIQeKysWW4w91Tl0QdikjregE4D3gj6kBERNJLibUI3L9jFP/OO4ucJa/A/25I7Kbnrofa7XD69ZCbl94A+40By4kgsTYbOvaG4p6t+1wRERGRNDlhUHfKSwq5d9pSdta28jIbIlFaDTwMaCa0iBzklFhrZXX1zqJ1VTze+/OhUcBLN8L8p/Z/U+Xs0LRg4Mkw+IPpD7J9ZzjkqJBY81ZabbRmB6x+R9NARURE5KCSk2NMHFvG+qpqHptbGXU4Iq3nXKCGULUmInIQU2Ktla3YuJ3q2nr69+gCF9wO7bvAg1fBxqWN3+AOT34vvD79BrBWWv2z7HjYthbWLWid561+C+prNQ1UREREDjoXjOxLu7wc7lQTA2lL8tBPmyLSJuhLXStbuLYKgIE9iqFrGZx7M2zfAPddDrXV+94w/wlYPAVGTIReR7VeoGXjwr7i5dZ5XuXssFdHUBERETnIdCks4OxjejNjyUbeXL4p6nBEWkcd8DwwI+I4RETSTIm1VrZwzVYADu1eFA4M/RAc/xVYPh2e+eGeF9fVwFPfh/wiOOX7rRto/4YGBq+2zvNWqCOoiIiIHLwuG1cGwF2vq2pN2pBTgJ9FHYSISHopsdbKFq4JFWsDehTtPnjKdSGR9dqf4O1Hdh+ffnuYinnCV6Bjr9YNtLgHlAxuvQYGlbOh+JDW/zhFREREWsGwvl04pm9nHpq5gvdjv2gVOajlAn8BPv//27vv+KiuM//jn2dGDQkQRQIBKkg002xTbcA1cbeT+GewHSekOHZs/xIn2TjObpLdOLvp7edk0902tuNN4rgkuLfgEgO26QZsqgAhkEACRJFA9fz+uCMYFAnEMDNXM/N9v17zGt17j66eo6srLo/OOY/fgYiIxJYSa3FWXnuQ7IwgBX2zju4MpsGcByA7D+Z9HnZvgkN18NoPvCqZM273J9iSmbCvouv136KlpQl2vadpoCIiIpLU/uXi0TS1tnH9PYt4v2q/3+GIxN4teKPWRESSmBJrcba5pp7SvBysYxGCvkNh9v3QeAAe+xS8+j04tAc+eBdkZPsTbEloOmhFjKeD7noPWptUuEBERESS2oVjBvGbj09m/6EWPnrvW6zcVud3SCIiInKKlFiLo4amFnbsO0xpXk7nDUZcCBd8DapXwTv3eomm06+Pb5DhitsLGMR4Omh74QKtryYiIiJJ7tLxBdz3qakcbm7l4/e/zeIte/wOSSR27gCK8NzZHqMAACAASURBVAoZiIgkKSXW4mhzeEXQrpz3VSi7wPv4ku9BwMdL1K8Y+hbGfsRaVahwgaaCioiISAo4f3Q+D31mOs45PvnAO7y5odbvkERiozdQCDT4HYiISOwosRZH7Ym1EfldjFgDCAThY3+Bz70FpefGKbIumEHJDKhZC/W7Y/d1qlZ668v1HRq7ryEiIiLSg5xdNpBHbj6L9KDxmYcW88p7O/0OSST6vg0sAvr4HYiISOwosRZHRyqCdjUVtF1aJgwaG4eIuiHW66y1NkP1am8aaMd150RERESS2KTi/vzplrPpnZnGbY8s5Zl3d/gdkoiIiJwkJdbiqDxUWv2EibWepDiUWIvVOms1a6G1UYULREREJCWNH5rLX249mwE5GXzxT8t5bEmMq7GLxNMm4IfASr8DERGJHSXW4mhzbT2D+mTSJyvd71C6L38MZA+Eihgl1toLF2h9NREREUlRIwf14bHbZjAktxdfffxd/rBoi98hiURHOfB1IMa10ERE/KTEWpw45yivqafseOur9URmXnXQqpXQeCD6598RKlygiqAiIiKSwkoG5vDYbTMozcvhm/PWcM/rm/wOSeTUTQfeAa73OxARkdhRYi1Oag42cqCxhdK841QE7alKZoJrg23vRP/cVSuhV3/ILYr+uUVEREQSyNB+vXj01rMZM7gPP3h+LXe/vB7nnN9hiUQuF5gGDPA7EBGR2FFiLU4213SjImhPVTzDe4/2OmutLVC9ypsGqsIFIiIiIgzqk8WfbzmbicNy+cXfN/D9595Xck0S2yFgj99BiIjEjhJrcVJe6yXWEm4qKEDB6ZDRO/qVQWvXQ8shTQMVERERCdM/J4P//exZTC3pz33/2Mx//G01bW1KrkmCGgJc53cQIiKxo8RanBytCJqAU0GDaVA0HSqXQEtj9M57pHCBKoKKiIiIhOublc7DN03nnJF5/O/bFdz5+EpaWtv8Dkvk5H0GuMzvIEREYkeJtTjZXFtPetAo6t/L71AiUzITWhth+7LonbMqVLhAFUFFRETiysxGmdlCM1tvZovNbHwnbT5gZu+Y2XtmtsbMfmxmenaMo+yMNO7/1FQ+eNognly2nS/9eQVNLUquSYK5G7jT7yBERGJHD0dxUl5TT/GAbNKCCfotL57pvVdEcZ21qpWQlQv9h0fvnCIiItId9wD3OudGAz8CHuykzV7go865ccAUYCbwybhFKABkpQf57dwpXDlxCM+uquK2R5ZyuLnV77BEREQkJEGzPImlubWNij0NiTkNtN2wKRDMiF4Bg7ZWqHrXmwaqwgUiIiJxY2aDgKnAI6FdTwBFZjYyvJ1zbrlzrjz08WFgBTA8jqFKSEZagP/+6JnMnlzI/LW7+MyDi6lvbPE7LJHueQi4GtjvdyAiIrGhxFocVOxpoKXNJWZF0HbpWV5yreJtLyl2qnZvhOZ6TQMVERGJvyKgyjnXAuC8kpMVQHFXn2BmBcAc4Jkujt9hZpXtL1WxjL60YICfzDmduWcXs3DTbj75P++w/3Cz32GJnNj7eL85av0OREQkNpRYi4PNNQlcETRc8QxoOgDVq079XCpcICIikhDMrC/wNPBj59ySzto45+52zhW2v0yj0WMiEDC+85EJ3HJeGUu37uVj973Fnvomv8MSOb7/ApqAMr8DERGJDSXW4qC8NoErgoYrmeW9Vyw69XPtCBUuGDrp1M8lIiIiJ2MbMMTM0gDMy4IV441aO4aZ9QFeAOY55+6Oa5TSKTPj65efxr9cNIrV2/fz0XsXsevAYb/DEulaJvpfp4gkNf2Ki4PyZBmxVjQdLABbF5z6uapWQEYf6F966ucSERGRbnPO7QKWAXNDu2YDlc65jeHtzKw3XlLtBefcd+MbpRyPmfEvF43mG1ecxvqdB7nud4vYXnfI77BEOrcfeBXY7HcgIiKxocRaHJTX1tM3K42BORl+h3JqsvpCwUTYughOZe2UtrajhQsC+hEUERHxwa3ArWa2HvgacCOAmd1vZh8OtfkSMB24xsxWhF7/7k+40plbzhvBd66ewJbdDVz3u0Vs3V3vd0gi/2w98AHgUb8DERGJDWU14qC8pp7S/N4kxXojxTOhoRZqN0R+jj3l3lptQ1W4QERExA/OuXXOuRnOudHOuanOuVWh/Tc7554Kffw951y6c+7MsNf3/I1cOvrE2SX89NozqNp3iGt/t4gNOw/4HZLIsUYAvwQu8zsQEZHYUGItxvYfbqb2YCMj8hJ8Gmi7kpnee8XCyM9RFVpfTYULRERERE7ZnCmF/PKGyeypb+L6e99i5bY6v0MSOao/cDugv6mLSJJSYi3GkqYiaLviGd771mgk1vSvq4iIiEg0XHn6EO75xBTqG1u47p5FPPPuDr9DEhERSQlKrMVYe0XQsvwErwjarnc+5I321lmL1I4VkJ4DA0dELy4RERGRFPfBsYN59NYZ9O2Vzu1/XM7PX1mPO5V1cUWi5TI0FVREkpYSazHWXhG0NFmmgoI3am1fBdRtO/nPdS5UuOB0CASjH5uIiIhICjuzqB9P3T6L8UP78vNXNvCFPy3ncHOr32FJqssBkmScgYhIR0qsxVh5bT1mSZZYK5nlvVdEMGpt72Zo3KdpoCIiIiIxMiS3F4/dNoPLxhfwzLtVXH/PInbtP+x3WJLKngAe9zsIEZHYUGItxspr6hma24us9CQanVVyCuusVa303lW4QERERCRmsjPS+M3HJ3P7hSNZWbmPD/9qAau37/M7LBERkaSjxFoMtbU5NtceTJ7CBe36FUPfwsgSaztChQuGasSaiIiISCwFAsadl47h59efyZ6GJq793SJeWF3ld1iSit4Gvg/U+h2IiEj0KbEWQ9X7D3O4uY2yZJoG2q5kJtSug/qT/NexagWk9YKBo2ITl4iIiIgc4+pJw/jTZ88mJzPIbY8s41fzN6iogcTXq8C/A+V+ByIiEn1KrMVQe+GCpKkIGq59OujJrLPmnDcVtGAiBNNiE5eIiIiI/JMpJf352+dncVpBH3760nq+/OgKFTWQ+JkLLAEm+B2IiEj0KbEWQ+W1B4EkK1zQrr2AwdaTSKzVVcChvZoGKiIiIuKDwv7ZPPF/Z3LR2MH8bcUObrjvLWoONPodlqSCQmAKkO13ICIST2aWZWZ/M7P1ZrbSzF42s5ERnGeUmS0MnWexmY0PO7bFzNaZ2YrQ6/ro9uLElFiLoaMj1pIwsZY3GrIHwtYF3f8cFS4QERER8VVOZhr3fmIKt50/guUVdXzkV2/y3o79foclqaAeOOB3ECLig3uBMc65M4B5wP0RnOMe4F7n3GjgR8CDHY5f75w7M/R69JSijYASazFUXltPZlqAobm9/A4l+sygeAZUvwuN3fwXsipUuGCIRqyJiIiI+CUQML52+Wn89NozqDnYyJzfLeSlNdV+hyXJbBPQG/iu34GISDw55w47555zRxf2fAsYDkdGoT0bGoH2rpnd3tk5zGwQMBV4JLTrCaAokpFvsaLEWgyV1xykNC+HQMD8DiU2SmaCa4Nt73Sv/Y4VkJYF+afFNi4REREROaE5Uwr542fPJis9yK2PLOW3r21SUQOJjcHAJ4DJfgciIlEUMLPKsNcd3ficLwHzzCwI/An4inNuGnA2cIuZTevkc4qAKudcC0AoSVcBFIe1edjMVpnZA2aWf0q9ioASazFyuLmV7XWHknMaaLuSmd771oUnbtteuGDweBUuEBEREekhpg0fwLzPz2LUoN786IW13PnYuzS2qKiBRFlv4GEg7isfiUgMtTnnCsNedx+vsZl9AxgJfB0YA4wH/mxmK4CFQB9gXARxnOecOx0vdV8LPBTBOU6JEmsxsnV3A85BWV4SVgRtN3giZPTuXmXQ/duhoVbTQEVERER6mKIBXlGDC8fk88SySj5+39vsPqiiBiIiEh1mdidwDXC5c64BMGBP2LpoZzrnSp1zD5nZJ8MKEdwIbAOGmFla6FyGN1qtAsA51/7eDPwcODfe/VNiLUbKa7yKoEk9Yi2YBkVnQeUSaDnBw5cKF4iIiIj0WH2y0rn/U9O4+ZxSlmzdy0d+vYB11VppXqLo28AtfgchIvEWmiJ6A3Cxc64utHsdsD+UOGtvN9LMBjjnHg5Ltv3eObcLWAbMDTWdDVQ65zaaWY6Z9Qv7cjcAy2Pfq2MpsRYj5bVeRdDSvCROrAGUzIDWRti+7PjtdoQKFwzViDURERGRnigYMP7jqnH8aPZEqvcd5prfLGD+2p1+hyXJ4h94S46LSMows0Lg/wH9gFdDo9DeDq2XdhVwTahwwRrgAaCryo+3Area2Xrga0B7Qm5w6Lzvmtkq4HzgkzHsUqe02FWMlNd4ibWy/CSeCgpQ3L7O2gIvydaVqhUQzID8sfGJS0REREQicv20YkoG5nDbI0u56aEl/PsVY7npnFK82TciEXoKyPI7CBGJJ+dcJd60z86ObQI+1M3zrAP+KeHgnCsHJp1KjNGgEWsxUl57kLzeGeT2Svc7lNgaNsVLmJ1onbWqlTBoHKRlxCcuEREREYnY2WUDmff5WZTl5fDdZ9/na0+soqmlze+wJJH1oov/XouIJDYl1mLAOUd5TX3yTwMFSM/ykmsVb0NbFxWk9lfBwZ2aBioiIiKSQEoG5vDXz8/ivNH5PLpkG3MfeJs99U1+hyWJqhr4O7DH70BERKJLibUY2NvQzL5DzcldETRcyUxoOgDVqzo/rsIFIiIiIgmpb1Y6//OpqXx65nDe2byHq3+9gLXV+/0OSxLR34CLgKV+ByIiEl1KrMVASlQEDXdknbWFnR+vChUuGKIRayIiIiKJJi0Y4D8/PJ7vXj2B7XWH+NAv3+Tul9ZxuLmL2QoinTkf+DUwxu9ARESiS4m1GGgvXJASU0EBiqaDBaCii8TajhUQSIPB4+Mbl4iIiIhEzdyzS3j8thmU5fXmF/M3csV//4NFm3b7HZYkirHA54BivwMREYkuJdZioLw2RSqCtsvqCwUTYesicO6fj1ethEFjIS0z/rGJiIiISNRMKu7PM188h3+9bAzb6w5xw31v8dXHVrJXa6+JiEiKUmItBsprDhIMGMUDsv0OJX5KZkFDLdRuOHb/wV1wYIemgYqIiIgkifRggM9dMJKXvnwe547K47GllXzw7tf56/JKXGd/ZBUBOABMAL7qdyAiItGlxFoMlNfWU9S/FxlpKfTtLZ7hvXecDqrCBSIiIiJJqWRgDg9/Zjo/u957zvvyoyv55P+8w9bd9T5HJj1SDtAGpPsdiIhIdKVQ5ic+Wlrb2Lq7PnWmgbYr6aKAwY5Q4YKhk+Ibj4iIiIjEnJnxfyYV8vc7zufaKYX8Y0Mtl/zsDX7z2kaaW9v8Dk96kgDwHvB9vwMREYkuJdaibHvdIZpbHWWpUrigXU4e5I321lkLV7UCLKjCBSIiIiJJrH9OBj+59gz++NmzGNqvFz9+YR0f+uWbLKvY63doIiIiMaXEWpQdqQian2KJNfBGre2rgLptR/dVrYT80yC9l39xiYiIiEhczByRx/NfOpcvfmAkm2oOMvu3C7lr3moOHG72OzTpCZ4Bfup3ECIi0aXEWpRtqjkIQFleik0FBSgOTQetCI1aq98N+7bBUBUuEBEREUkVWelB7rhkDM998VymlvTn4UVbueju13lhdbXfoYnfHsIrXtDidyAiItGjxFqUba71RqyNSMkRa6ECBlsXeO9VofXVVLhAREREJOWMGtyHR2+ZwQ+umcihplZue2Qpn314CTvqDvkdmvjl28BSwPwOREQkepRYi7Lymnp6Z6aR3yfT71Dir18x5BYdXWftSGJNI9ZEREREUlEgYNwwvZhXvnI+V50+hJff28nFd7/O7xdsprXN+R2exNtYYDIQ9DsQEZHoUWItysprD1Kal4NZiv4ZpngG1K6D+lqvIqgFoGCC31GJiIiIiI8G9cniVx+bzO9vnEa/7Az+6+n3uOY3C3hvx36/Q5N4csBB4LDfgYiIRI8Sa1FU39jCzv2NlKXiNNB2JWHrrFWt9CqFZqTw90NEREREjrhwzCBevuM8bjmvjNU79vOhX73JD557n4YmLbqVEh4D+gBP+R2IiEj0KLEWRe3rq6Vk4YJ27Ym1tc9B3VZNAxURERGRY2RnpPGNK8Yy7/OzGD+0L/e8Uc4lP3uD19bt8js0ibUxwKeAQr8DERGJnpgn1sxslJktNLP1ZrbYzMZ30iZgZneb2Xtm9q6ZvWpmI8OOX2Vma81sg5k9aWZ9Yx13JNorgpam8oi1vNGQPRBWP+5tqyKoiIiIiHRiwrBc/vq5Wdx11Tj21Dfx6d8v5ot/Wk7NgUa/Q5NYOQN4EJjpcxwiIlEUjxFr9wD3OudGAz/C+1Xa0YeBWcAZzrnTgb8D3wcws97AA8DVzrlRwA7gm3GI+6QdHbGWwok1M2+dtdYmb1sVQUVERESkC8GA8ZlzSnnljvO5aOxgnlq5g1k/nM/NDy3myWWV7D/c7HeIIiIix5UWy5Ob2SBgKnBJaNcTwK/MbKRzbmNYUwdkAllm1gL0BSpDxy4Hljvn1oa2fwO8BHw1lrFHorzGS6yVpnJiDaBkFqx9BjAomOh3NCIiIiLSww3t14v7PjmFl97byR8WbWX+2l288v4uMoIBzh2VxxUTh3DRuMHk9kr3O1Q5FQ64GRgN/JvPsYiIRElME2tAEVDlnGsBcM45M6sAioHwxNrTwIVANXAA2A6cHzpWDGwNa7sFGGJmae3n7SnKaw9S0DeLnMxYf1t7uJIZ3vvAkZDZx99YRERERCQhmBmXji/g0vEF7D7YyItrdvLcqipeW1/D39fuIj1onDcqX0m2RGbAX4HJKLEmIkmjp2SApgITgGHAfuCHwO+AuSdzEjO7A7ijfTs3NzeKIR6fc47NNfWcUdQvbl+zxxo8EfoPh1GXnLCpiIiIiEhHA3tn8rGzivnYWcXsqW/ixTXV/5RkOzeUZLtYSbbEUgGk+AQfEUkusU6sbSNsdJmZGd4ItIoO7T4JzHfO1QGY2UN40z0Jtb04rO1wwkbBhXPO3Q3c3b5dWFjootWRE9l1oJH6plbKUrlwQbtgGnxhGZiKzoqIiIjIqRmQk8EN04u5YbqXZHtpTTXPrqri9fU1zA8l2c4ZmceVpw9Vki0R9PY7ABGR6IppYs05t8vMluGNPHsQmA1UdlhfDaAcuMLMfuqcawKuAlaHjr0A/NrMTguts/Y54M+xjDsSRyqC5ulfCgACQb8jEBEREZEkMyAng49OL+ajHZJsb2yo5dV1NUeSbFdMHMIl4wrIzVaSrccpD70+QHxK6YmIxFg8poLeCjxoZt/Am+Z5I4CZ3Q885Zx7Cvg1MBZYaWbNeGut3QbgnDtgZjcDfzOzNLyE26fiEPdJaS9coBFrIiIiIiKxF55k21vfxEvvVfPsquojSbZvBFcxK5Rku1RJtp7jh8B9QC0w0OdYRESiwJyL22zJuCssLHSVlZUnbhgF33nmPR54czNvfPVCigdmx+VrioiIpDoz2+6cK/Q7DjlWMBh0ra2tfochKSo8ybZwYy0tbY60gHHOKCXZeoT5wDrgE2haqEgCM7NW51xPWbffV0qsRcmNv3+HBRt38/53LiMYsLh8TRERkVSnxFrPpMSa9BR1DU28tGYnz66qYkEoyZYeNC4eN5jrphZx7qh8Pbv75c/ADo6WnlsPPAF8CK+sHcCvgL54K3IDLARex5sDVQAcBn4GnAFcEWrzFLAWuAEoimkPRFKaEmtHKbEWJef/5FUyggFevuP8uHw9ERERUWKtp1JiTXqi9iTbvJXbWbBxNwBDc7OYM6WQa6cWUTRAs07i6hLgbWBfaHsecDXwB7wVugEK8UrfLQxtfx/4d2AZMCn0uf2Am4D7Q23mhto/A4yLaQ9EUpoSa0cpsRYFTS1tnPbN57l43GDu+cTUmH89ERER8Six1jMpsSY9XcXuBh5buo3HllRSvf8wZjBrRB7XTSviknGDyUpXIa6Y2wDUA2eGtuvwihoMBwaE9q0CMoAxoe1qvFFupwHZQCuwEm+ttpJQm81AEC8hJyIxo8TaUfomREHFnnraHJTla5EAEREREZGernhgNl+5ZAz/ctFo3thQw18Wb+OV93fy5sZacnul838mDeP6aUWMHdLX71CT16gO2/2AyR32TeywXRB6tQt28jmlYR8vBZ4F7oowRhGRblBiLQo2hSqCluapIqiIiIiISKIIBowLxwziwjGDqD3YyF+XbefRJdt4cOEWHly4hdMLc7luahEfPnMofbNU8CDhfBOvWMLHgRE+xyIiSUtTQaPgd69v4ofPr+WJ/zuDKSUDTvwJIiIiEhWaCtozaSqoJDLnHMsq6vjL4m08/e4OGppayUoPcMWEIVw3rYizSgdgpoIHCaEWb2roNL8DEUk+mgp6lL4JUVBecxCAsjxNBRURERERSWRmxpSS/kwp6c83PzSOZ9/dwaOLt/Hk8u08uXw7pXk5XDu1kDmTCxnUN8vvcOV48kIv8NZwqweG+ReOiCQnjViLgjm/XcjGmoOsuOuSmH8tEREROUoj1nomjViTZLRh54EjCbY99U2haaT5XD+tmAvH5JMWDPgdonTlEF4V0V7AW0Cmv+GIJAONWDtK34Qo2FxbT5nWVxMRERERSVqjBvfhP64ax79edhp/f38njy7Zxvy1u3jl/V3k98lk9uRCrptaqIJmPVEv4BNAX7wqoyIiUaQRa6doX0MzZ3z7JeZMKeSn154R068lIiIix9KItZ5JI9YkVeyoO8TjSyv5y5JtVO49BMD04QP4wNhBTBvenwnDcslMC/ocpYhI9GnE2lH6JpyiTbXe+mqqCCoiIiIiklqG9uvFFz84itsvHMmi8t08ungbL6yp5p0tewDISAtwRmEuU4cPYNrw/kwpHkButqqL+qoBuBm4HviIz7GISFJQYu0UldfUAzAiX4k1EREREZFUFAgYs0bmMWtkHg1NLazYVseSLXtZvGUPyyvqWLxlL78NtR09uDdThw9gakl/pg0fQGH/XqoyGk+1wMtAOkqsiUhUKLF2ijaHRqxpLQUREREREcnOSGPmiDxmjvDKUba2OdZW72fJlr0s2bqXxZv38Me3K/jj2xUADO6beUyi7bSCPiqEEEvFwNvAcJ/jEJGkocTaKSqvqccMSgZm+x2KiIiIiIj0MMGAMX5oLuOH5vKpmcNxzrG97hBLt3oj2pZs2ctzq6p49t0qAHIygkwu6c+UUKLtzKJ+5GTqv21RVRb28V+Bq/BGsImIREC/oU9ReU09hf17aVFSERERERE5ITOjsH82hf2z+ciZwwDYd6iZZRV7WRJKtL2zeQ//2FALeIm5cUP6MnW4l2ibWtKfQX2z/OxC8ngU+CjwLeA//Q1FRBKXEmunoK3NsXl3PTPKBvodioiIiIiIJKjcXulcOGYQF44ZBEBTSxurd+xjaWidtqVb9/L7Bfv4/YItAIwa1JvLJhRw6fgCxg/tqzXaInUN8G/AF/wOREQSmTnn/I4hZgoLC11lZWXMzr9tTwPn/vhVbpw1nG99aHzMvo6IiIh0zsy2O+cK/Y5DjhUMBl1ra6vfYYgkDeccm2vrvdFsW/bw2rpd1B5sAqCwfy8uG1/AZRMKmFzcn0BASbaINQEZfgchkhjMrNU5p8FaaMTaKSmv9SqCluWpIqiIiIgkDjMbBTwE5AH7gE8759Z0aDMceBCYBGx2zp0Z3yhFpJ2ZUZbfm7L83lw3rYjWNsfSrXt5YXU1L66p5v43N3P/m5vJ75PJpeMHc9n4IZxVNoB0FUHovseBO4HXgRKfYxGRhKLE2inYXKOKoCIiIpKQ7gHudc49aGZz8BJo0zq02Q/8B5ALfC++4YnI8QQDxvTSAUwvHcA3rxrL6u37eWFNFc+vruaRtyp45K0Kcnulc9HYwVw2oYBzR+WRla41oY8rDWgGalFiTUROiqaCnoK75q3m4UVbWfT1DzAkt1fMvo6IiIh0TlNBT56ZDQI2AgOccy3mLc5UBZzjnNvYSfsLgJ+fzIg1TQUV8c/GXQd4flU1L6ypZs2O/QBkZwS5cMwgLp1QwIVj8umTpRKYnWoAsv0OQiQxaCroUfomnILymnp6pQcZ3EdVeURERCRhFAFVzrkWAOecM7MKoBgv4SYiCWzkoD584YN9+MIHR7FtTwMvrqnmhdXVPLe6imdXVZERDHDOqDwuG1/AReMGMyBHi4od0Z5UWw+8CtzqYywikjCUWDsFm2vrKc3L0QKhIiIiktLM7A7gjrBtH6MRkXZFA7K5+dwybj63jF37D/PSezt5cU01b6yvYf7aXQSehLNKB3L5xAIuGVdAQa4GDABwG/AP4BKg1OdYRKTHU2ItQoeaWtled4hJxf38DkVERETkZGwDhphZWthU0GKgItITOufuBu5u3w4Gg8m71ohIghrUN4u5Z5cw9+wS6hqa+Pv7u3ghlGRbVL6bu+atYVJxPy4bX8DlE4ZQPDCF50T+HtiAkmoi0i1KrEVosyqCioiISAJyzu0ys2XAXLyiBbOBys7WVxOR5NQvO4PZUwqZPaWQ+sYWXltXwwtrqpn//k6WV9Txg+fXMm14f66dUsQVpw+hd2aK/bexhKMFDA4Bh4H+/oUjIj1biv2GjJ4jiTVVBBUREZHEcyvwoJl9A6/6540AZnY/8JRz7ikzy8ZbaSgTyDWzSuAPzrmv+xW0iERfTmYaV54+hCtPH8Lh5lYWbKzlqZU7eGF1NYu37OVbT63h8gkFzJlayNmlA1NrGZwG4HygD/AioJoPItIJJdYiVF5zEICyfI1YExERkcTinFsHzOhk/81hHzcAqrgqkkKy0oN8cOxgPjh2MPsPN/PMyioeX7qNJ5dv58nl2xnWrxezpxQyZ3JhakwVzQbOBgYAQZ9jEZEey5xL3iUwCgsLXWVlZUzO/eVHdW99tAAAEwxJREFUV/DX5dtZ9Z+XqFy1iIiIT8xsu3NOyZ8eJhgMutbWVr/DEJEo2VRzkMeXVvLkskp27m8E4KzSAcyZUsgVE4eQk8xTRR2QQoP0RLrLzFqdc0l883efEmsR+siv3mTHvsMs/veLYnJ+EREROTEl1nomJdZEklNrm+PNjbU8vrSSF9dU09TSRnZGkMsnDOHaqYVMHz4geaeKNgN3AnOAc32ORaQHUGLtKH0TIuCco7y2nnFD+vodioiIiIiISFwEA8b5o/M5f3Q++xqaefrdHTy+tJInlnmvogG9mD25kNmTCykakGRTRTcDDwDVKLEmIsdQYi0CtQebOHC4RYULREREREQkJeVmpzP37BLmnl3Cxl0HeGxpJX9dtp2fv7KBn7+ygRllA5kzpZDLJxaQnZEE/+0cDfwDmOB3ICLS0wT8DiARHSlckKfCBSIiIiIiktpGDurD1y8fy8KvfYDf3ziNKycOYenWvXzlsZVM++4r/OvjK3ln8x4SfhmiSRytDDoLr75yu18ApwNbQ9s7Q9s/CmvzJWBy2PbCUJsnwvZdBVwbtr0E+Dmw4xRjF5GYSYI/HcTf5tp6QBVBRURERERE2qUFA1w4ZhAXjhlEXUMTT6/0por+ZYn3KhmYzezJhVx95jDy+2SSkRYgmKhrsjXirbvWriW0z3Vo0xK23Qw0hW23hdqEL0nZxLEVSF8Avok3/XQocAgowEvq/TjU5hVgD3AN+h++9ChmNgp4CMgD9gGfds6ticJ5s/EmZ0/Du5O+4Zx7/ETHYkXFCyLw/efe5943ynn1zgso1ag1ERER36h4Qc+k4gUiEm79zgOhqqLbqT3YeMyxgEFGWoCMYOCY9/T27dDHmaFjne5PC5AeNDKCwSMfZ6YHyQwGyEw/es7MtGDo/eg5MtOOHmv/Gj2qAMMu4H28FEE23ki4jwNXAl8OtbkS+DvQgDcn7Q3gs3ij5a4OtXkWGAxMjVfgEgvOOdqcV0iktc3R6tzRj9scbc7R0uZoa/PeM9MCDO3XKyaxdKd4gZnNBx52zj1oZnOAf3POTTuJr7HFOTe8k/13AWXOuU+bWSnwNjDWObf7eMdOonsnRYm1CNz80GJeW1fD+9+5jPSgZtOKiIj4RYm1nkmJNRHpTEtrG29sqGH+2l00NLbS2NpGc0sbTa1tNLW00Rx6b2z/+Mh+R1OL93FTa1vM4/SSdAEy04Oh9/DknPeeFghgHfJvFrbDjuyjk33Haxd2zLyt9mOGHf3YvG0Mhm3txYDdGayZsh8DxqzqwzUPD2Pe3O2sm+QtY/S9myawo+Qwv/3WJgDOeXEg5zyXz2+/uZG6gc302ZPG7d8eyaIL9vDyh3finOOj9xdTtiGH7/zgPZyDkWtz+MQDw3ny2u0snboXcHzlh2No6NXCL7+wEefgvDfyufL5Ifzi1g1UFDeQ1gzf/u5Elp6xl0evrsA5uPr5YUxa3Y/vfeF9Dme2MnhnFp/5SynzZ+5i4aRaHPDxecVkH0rjnuvLwTlGb+7DRYsG8/R529k8tAGATz81nF35h3n5nF0EA8b0dwcwcUNf5l21g4Y+rfRtSOMjzwxj/WkHWTW1jmDAOOfVPAbvzOLZuVUEAsaQyizOmj+Q1efWUTXmMIGAcf6j+bT0ciy5Zi9tzlG4vBej3unNgit3sye/CWuES/53MBVlDSyZsZfWNsekRf0o2pTNk9dspymtjdzadC56ZRBLx9exevQ+2tocl7w6mKzDQf5y8TZa26BsazazVubx4uSdlOcfpK0NbnxpOJX9DvHUpB20tjlmrBvI5K39ue+scvZkNZHTEOS2t0fwTuEeXhtRA8DsVcMorsvhZ+euB2DE7hxmry7k+dHV5MwK8udbZnT7Z/9knCixZmaDgI3AAOdci3k/+FXAOUB/vNRvX7wxmt93zj3WyTm6SqytAW5yzr0V2v4L8JJz7v7jHTu1HndNA0UjUF5bT/HAbCXVREREREREuiktGOADpw3mA6cNjvgczjkv0dYhKXfkvcPHjS1tNLa0Htnf2Bz+3nqkzbHtO/+cA4dbqA3tb2kLDVA59u1IjP+8r72dO2b7mHbH7HM4d3SXc95ndjkuJg1YeXTzJ3PXeR8s987bcHErh9NbeX2Zl4wp2JDFmLY+PLe6muq+h8k/mMn1bUWs3FnH0yu9Bd2m7htA79Ygz6+uBmB6ZX+ucENZWVXH6+t3YRhzm0s4GGzh7fI9mMGwvVnU2UDW1uxnY+AgWc0B2hoddfubWFd9ADPD7XTk7cykfPdBGjMcmdVBTtvYh9dLatheegjDGLcml/716dRcfhgzY3pFOpcuKuCVMTvZP6AZw/jQG0NZXrqXP0ysoNU5PrxqKFcsGMovx29kW79DDN6Tya9fKqCirpyXsnfiHMx+rZCZW/O4afwSAC7YlM9XnhnDvNbtPLbHG5TzmWdK2durid8N9JKQt7xdxidfG84Pc9eycug+spoDfO+ZiTwxoZJ5vbzv1fmv5XPJ6gLuHL+Sxsw2zqjO5coXh7K+9SAb+x0kYMashXkMrM/g3lnlBANG0Y5sPja/hLVlB6gtaiQYMD6+oIT3R+3nvUv3kxYwPrRoCFcuHMqmqw+wZ3Azg/Zk8rn/HsmYK3cyYFIGgYBx08uljFjdm9qveucYs6wPN9xfzPAZOew/M3y+ctQFzCx8JNPdzrm7w7aLgCrnXAuAc86ZWQXeyoLfBK5wzlWZWR6wzMwWOue2d/NrF3N0RUOALaF9JzoWExqxFoF9Dc3U1jcyQlVBRUREfKURaz2TRqyJiMSec10n3zom8MITe+2f0z4aLhAaBtc+Cq59X/tx6zg0Lx4c3tpz7UOBmoB6IAfICO2rBLLwVu8CqAMO4q1Bl4a3vl010Bvo531/Wnc52g5D29DQ9MkGh6t1tPR1tGY72trAVTnacLTmQyAAaQeNtAMGg41glhHASNthBHobgXwImhHcY1iDeamkAN7aeTvwxmX1C8W3PRRTSWj7AN5U34JQvwDKgV7AkND27lC/ivEKZzQDFUBuWL+r8NbeKwtt14f6nY83HixGujFibQrwR+fcmLB97wD/CfwJ2BzWfADe+mvzzewVjvZuHPBeWLvpzrkmMzsAjHbOVYXO+2PgsHPuruMdO8Uud0kj1iKQm51Obnb6iRuKiIiIiIiIxICZdZiO2oPWhjtVxrHZigyOJtTadfyzWj+OJrEIfX5YGzMjbXCH71EmXvIrXG6H7Y7HAUZ12M7vsJ0JlHbYN6zDdp/QK1xZh+2BoVe7dGBEhzZDOmzndNLGH9uAIWaWFjYVtBjvu7fGOTezs09yzl3U/nFoKuiZnTSrwEtRVoW2hwMvdeNYTGguo4iIiIiIiIiIRI1zbhewDJgb2jUbb5zhw0CpmYUn0M40s46p0+N5DLgt9LmlwAXA37pxLCaUWBMRERERERERkWi7FbjVzNYDXwNudM7txatl+w0zW2lm7wE/5OTyUz8BepnZJuBF4HbnXG03jsWE1lgTERGRhKU11nomrbEmIiKS3E60xloq0Yg1ERERERERERGRCCixJiIiIiIiIiIiEgEl1kRERERERERERCKgxJqIiIiIiIiIiEgElFgTERERERERERGJgBJrIiIiIiIiIiIiEVBiTUREREREREREJAJKrImIiIiIiIiIiERAiTUREREREREREZEIKLEmIiIiIiIiIiISASXWREREREREREREImDOOb9jiBkzawRqYnT63sDBGJ27p0q1Pqdaf0F9TgWp1l9Qn5NdvnMu0+8g5Fhm5oDWGJ0+ALTF6Nw9Uar1F9TnVJBq/QX1OVWkUp+DzjnzO4ieIKkTa7FkZpXOuUK/44inVOtzqvUX1OdUkGr9BfVZJNmk2s93qvUX1OdUkGr9BfU5VaRin0VTQUVERERERERERCKixJqIiIiIiIiIiEgElFiL3N1+B+CDVOtzqvUX1OdUkGr9BfVZJNmk2s93qvUX1OdUkGr9BfU5VaRin1Oe1lgTERERERERERGJgEasiYiIiIiIiIiIRECJNRERERERERERkQgosXYcZjbKzBaa2XozW2xm47tod5OZbTCzTWZ2n5mlxzvWaDCzLDP7W6i/K83sZTMb2Um74WbWamYrwl4j/Ij5VJnZFjNbF9aP67tolyzXeGCH67bezFrMbECHdgl9jc3sF6Fr68zszLD93bqnQ20T5pp31t/u3s+htgl3vY9zjbt1T4faJsw1hi6vc7fu6VDbhLvOkrpS6RksFZ+/QM9gyfgMlmrPX6BnsFR4BtPzl3SLc06vLl7AfODToY/nAIs7aVMK7AAKAAOeAj7vd+wR9jcLuIKja+/dDrzWSbvhQJ3f8Uapz1uAM0/QJmmucSd9uxN4OtmuMXAeUNjx+nbnnk7Ea95Zf7t7Pyfq9T7ONT7hPZ2I1/h4fe7QptN7OlGvs16p+0qlZ7BUfP4K9UfPYEn2DJZqz19d9VnPYCf8/IS6znr+0qs7L41Y64KZDQKmAo+Edj0BFHXy14Y5wFPOuWrn3Tm/A26IX6TR45w77Jx7LtQPgLfwfhGkuqS5xp24CXjA7yCizTn3hnOuMnzfSdzTkGDXvLP+Jvv93FmfT1JCXWPodp+T8p6W1JJqz2DJ/vv6FCXFNe5C0v2+TrXnL9AzWIQS6jrr+Uu6Q4m1rhUBVc65FoDQTV8BFHdoVwxsDdve0kmbRPUlYF4Xx3JCQ7mXmdldZhaMZ2BR9rCZrTKzB8wsv5PjSXmNzWwm0B94posmyXSNofv3NCTnNT/e/QzJdb1PdE9DEl7jbtzTkFzXWZJXqj+DpcrzF+gZLBWewVL9+Qv0DNZRUl1nPX8JKLEmXTCzbwAjga93crgKGOacmwZcBJwLfCWO4UXTec6504HJQC3wkM/xxNNNwMPtDzodJNM1TnknuJ8hua637unO72lIrusskpRS6PkL9Ptaz2ApQM9gKUHPX6LE2nFsA4aYWRqAmRleJr2iQ7sKoCRse3gnbRKKmd0JXANc7pxr6HjcOdfonNsV+ngP8D94vyASjnOuIvTeDPyczvuRjNe4N3Ad3rX7J8l0jcN0956GJLrmJ7qfIbmudzfvaUiiawwnvqchua6zJL2UfAZLpecv0DMYqfMMlpLPX6BnMFLgGUzPX9JOibUuhH74lwFzQ7tmA5XOuY0dmj4BfNjMCkL/UNwG/Dl+kUaXmd2BN8f9YudcXRdtBrVXbjGzTLx/MJbHL8roMLMcM+sXtusGOu9HUl3jkOuBlc65tZ0dTJZrHO4k7mlIkmvenfs51C4prvdJ3NOQJNc4zHHvaUie6yzJLxWfwVLp+Qv0DEYKPYOl4vMX6BmM1HkG0/OXeFwPqKDQU1/AGGARsB5YAkwM7b8f+HBYu88Cm0KvB4B0v2OPsL+FgAv1Y0Xo9Xbo2LeB20IfXwOsBlYCa4BfApl+xx9Bf8vwfqm9C6zCW/tgeDJf47D+LARu7LAvaa4xcA9QCbQAO4GNof2d3tOJfs076+/x7udkuN5d9LnLezrRr/Hxfq5Dx/7pnk6G66xX6r66+n2d6PdxF31NqeevUF/0DHbsvqS4zl39O9XV/ZwM17uL5xE9gyXRM1hXP9ehY3r+0gvn3JESwCIiIiIiIiIiInISNBVUREREREREREQkAkqsiYiIiIiIiIiIRECJNRERERERERERkQgosSYiIiIiIiIiIhIBJdZEREREREREREQikOZ3ACIi4cxsC9AIHArb/Qnn3Koofo3hwArnXL9onVNEREQkUen5S0QkckqsiUhPdL1zboXfQYiIiIikED1/iYhEQFNBRSQhmJkzs++a2XIzW29mHw87dqmZLTOzd83sdTMbF3bsRjNbYWYrzWxJ6K+l7cf+y8yWmtlGM7sivj0SERER6dn0/CUicmIasSYiPdGjZhY+FWFG6N055yaZWRmwxMwWAA3AH4ELnHOrQg98j5vZeOB84C5gpnOuysyyQ+cZBOQC7zrnvmVmlwH/DTwXh76JiIiI9ER6/hIRiYA55/yOQUTkiNAaH1d3nIpgZg4Y7pzbGtr+G/AksBf4inPugrC2dcAE4EvAIefcXR3ONRx4H8h2zjkzywV2O+f0xwYRERFJOXr+EhGJnKaCikgiO5W/DDS6o39ZaAWCUYhHREREJNnp+UtEJIwSayKSSG6EI3/xPBf4B/AWMNHMJoSOfRTYHno9Dcw1syGhY9lh0xFERERE5MT0/CUichwadisiPVHHNT6+HHoPmtlyIAf4onNuC0BoXY+HzSwNb2rCtaG/hr5hZv8FvBiaytAEzIlXJ0REREQSiJ6/REQioDXWRCQhhB7M+jvn6vyORURERCQV6PlLROTENBVUREREREREREQkAhqxJiIiIiIiIiIiEgGNWBMREREREREREYmAEmsiIiIiIiIiIiIRUGJNREREREREREQkAkqsiYiIiIiIiIiIRECJNRERERERERERkQgosSYiIiIiIiIiIhIBJdZEREREREREREQi8P8BsC7cYe9/XZ8AAAAASUVORK5CYII=\n","text/plain":["<Figure size 1440x480 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"19hmvo8AhIeW"},"source":["##**T5XL_UNI:**"]},{"cell_type":"code","metadata":{"id":"gypbtvmWWaWV"},"source":["X_train, X_val = load_X_data('T5XL_UNI')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ZVNAWPkSSkCA","executionInfo":{"status":"ok","timestamp":1629219450448,"user_tz":-60,"elapsed":12574327,"user":{"displayName":"Will Dee","photoUrl":"","userId":"18339245303983317021"}},"outputId":"2c323925-88df-45e2-c798-0d9c354fb6ca"},"source":["filepath = 'Keras_Models/T5XL_UNI_best_model.epoch{epoch:02d}-loss{val_loss:.2f}.hdf5'\n","Plots_Path = 'Training_Plots/INDEP/T5XL_UNI_best_model_plot2.png'\n","train_complex_model(X_train, y_train_res, X_val, y_val_res, filepath, Plots_Path, 30, 8, False, 352, 27, 'RandomNormal', 2, 4, 128, 21, 2, 4, 640, 64, 0.0, 0.0001, 'Adam')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-2bcccdb2-75bf-3509-d313-72a6ec56e72e)\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 255, 1024, 352)    256960    \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 64, 256, 352)      0         \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 64, 256, 352)      256       \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 64, 256, 128)      19869824  \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 16, 64, 128)       0         \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 16, 64, 128)       64        \n","_________________________________________________________________\n","flatten (Flatten)            (None, 131072)            0         \n","_________________________________________________________________\n","dense (Dense)                (None, 640)               83886720  \n","_________________________________________________________________\n","dropout (Dropout)            (None, 640)               0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 64)                41024     \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 64)                0         \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 65        \n","=================================================================\n","Total params: 104,054,913\n","Trainable params: 104,054,753\n","Non-trainable params: 160\n","_________________________________________________________________\n","Epoch 1/30\n","376/376 [==============================] - 706s 2s/step - loss: 1.8628 - accuracy: 0.5394 - val_loss: 0.5740 - val_accuracy: 0.7254\n","Epoch 2/30\n","376/376 [==============================] - 657s 2s/step - loss: 0.6116 - accuracy: 0.7408 - val_loss: 0.4031 - val_accuracy: 0.8198\n","Epoch 3/30\n","376/376 [==============================] - 658s 2s/step - loss: 0.2802 - accuracy: 0.8925 - val_loss: 0.4454 - val_accuracy: 0.8225\n","Epoch 4/30\n","376/376 [==============================] - 658s 2s/step - loss: 0.1649 - accuracy: 0.9575 - val_loss: 0.8213 - val_accuracy: 0.6636\n","Epoch 5/30\n","376/376 [==============================] - 658s 2s/step - loss: 0.3665 - accuracy: 0.8422 - val_loss: 0.3198 - val_accuracy: 0.8511\n","Epoch 6/30\n","376/376 [==============================] - 658s 2s/step - loss: 0.0362 - accuracy: 0.9941 - val_loss: 0.4281 - val_accuracy: 0.8670\n","Epoch 7/30\n","376/376 [==============================] - 658s 2s/step - loss: 0.0102 - accuracy: 0.9992 - val_loss: 0.3185 - val_accuracy: 0.9056\n","Epoch 8/30\n","376/376 [==============================] - 658s 2s/step - loss: 0.0044 - accuracy: 1.0000 - val_loss: 0.3341 - val_accuracy: 0.9069\n","Epoch 9/30\n","376/376 [==============================] - 658s 2s/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.3375 - val_accuracy: 0.9076\n","Epoch 10/30\n","376/376 [==============================] - 658s 2s/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.3525 - val_accuracy: 0.9082\n","Epoch 11/30\n","376/376 [==============================] - 658s 2s/step - loss: 8.4011e-04 - accuracy: 1.0000 - val_loss: 0.3657 - val_accuracy: 0.9089\n","\n","Epoch 00011: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n","Epoch 12/30\n","376/376 [==============================] - 658s 2s/step - loss: 6.4439e-04 - accuracy: 1.0000 - val_loss: 0.3598 - val_accuracy: 0.9116\n","Epoch 13/30\n","376/376 [==============================] - 658s 2s/step - loss: 5.9500e-04 - accuracy: 1.0000 - val_loss: 0.3640 - val_accuracy: 0.9096\n","Epoch 14/30\n","376/376 [==============================] - 658s 2s/step - loss: 5.7981e-04 - accuracy: 1.0000 - val_loss: 0.3623 - val_accuracy: 0.9102\n","Epoch 15/30\n","376/376 [==============================] - 658s 2s/step - loss: 5.1908e-04 - accuracy: 1.0000 - val_loss: 0.3623 - val_accuracy: 0.9122\n","\n","Epoch 00015: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n","Epoch 16/30\n","376/376 [==============================] - 658s 2s/step - loss: 5.1990e-04 - accuracy: 1.0000 - val_loss: 0.3642 - val_accuracy: 0.9102\n","Epoch 17/30\n","376/376 [==============================] - 658s 2s/step - loss: 5.1999e-04 - accuracy: 1.0000 - val_loss: 0.3811 - val_accuracy: 0.9056\n","Epoch 18/30\n","376/376 [==============================] - 658s 2s/step - loss: 4.7655e-04 - accuracy: 1.0000 - val_loss: 0.3672 - val_accuracy: 0.9102\n","Epoch 19/30\n","376/376 [==============================] - 658s 2s/step - loss: 5.0489e-04 - accuracy: 1.0000 - val_loss: 0.3670 - val_accuracy: 0.9096\n","\n","Epoch 00019: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x480 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"7jX3K_EMhics"},"source":["##**T5XL_BFD:**"]},{"cell_type":"code","metadata":{"id":"SeKzFwgqhlRB"},"source":["X_train, X_val = load_X_data('T5XL_BFD')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"ryeCGgT3eQVQ","executionInfo":{"status":"ok","timestamp":1628677605788,"user_tz":-60,"elapsed":4602958,"user":{"displayName":"Will Dee","photoUrl":"","userId":"18339245303983317021"}},"outputId":"4de42261-4460-4449-c2ff-b36a1e03d81f"},"source":["filepath = 'Keras_Models/T5XL_BFD_best_model.epoch{epoch:02d}-loss{val_loss:.2f}.hdf5'\n","Plots_Path = 'Training_Plots/INDEP/T5XL_BFD_best_model_plot.png'\n","train_model(X_train, y_train_res, X_val, y_val_res, filepath, Plots_Path, 30, 8, False, 384, 25, 'RandomNormal', 8, 0.0001, 'SGD')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-7bf31444-b3e9-de40-91c0-e571b05336bb)\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 255, 1024, 384)    240384    \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 32, 128, 384)      0         \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 32, 128, 384)      128       \n","_________________________________________________________________\n","flatten (Flatten)            (None, 1572864)           0         \n","_________________________________________________________________\n","dense (Dense)                (None, 1)                 1572865   \n","=================================================================\n","Total params: 1,813,377\n","Trainable params: 1,813,313\n","Non-trainable params: 64\n","_________________________________________________________________\n","Epoch 1/30\n","376/376 [==============================] - 242s 561ms/step - loss: 1.4217 - accuracy: 0.6683 - val_loss: 0.5298 - val_accuracy: 0.8278\n","Epoch 2/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.4249 - accuracy: 0.8672 - val_loss: 0.4860 - val_accuracy: 0.8757\n","Epoch 3/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.2064 - accuracy: 0.9263 - val_loss: 0.3937 - val_accuracy: 0.8830\n","Epoch 4/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.1209 - accuracy: 0.9560 - val_loss: 0.3918 - val_accuracy: 0.8757\n","Epoch 5/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.1058 - accuracy: 0.9681 - val_loss: 0.3337 - val_accuracy: 0.8903\n","Epoch 6/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0608 - accuracy: 0.9849 - val_loss: 0.4776 - val_accuracy: 0.8703\n","Epoch 7/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0473 - accuracy: 0.9901 - val_loss: 0.3367 - val_accuracy: 0.8910\n","\n","Epoch 00007: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n","Epoch 8/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0378 - accuracy: 0.9969 - val_loss: 0.3272 - val_accuracy: 0.8943\n","Epoch 9/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0371 - accuracy: 0.9963 - val_loss: 0.3262 - val_accuracy: 0.8956\n","Epoch 10/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0362 - accuracy: 0.9939 - val_loss: 0.3253 - val_accuracy: 0.8976\n","Epoch 11/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0355 - accuracy: 0.9965 - val_loss: 0.3268 - val_accuracy: 0.8943\n","Epoch 12/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0362 - accuracy: 0.9971 - val_loss: 0.3286 - val_accuracy: 0.8976\n","\n","Epoch 00012: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n","Epoch 13/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0357 - accuracy: 0.9966 - val_loss: 0.3261 - val_accuracy: 0.8963\n","Epoch 14/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0352 - accuracy: 0.9965 - val_loss: 0.3286 - val_accuracy: 0.8936\n","\n","Epoch 00014: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.\n","Epoch 15/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0361 - accuracy: 0.9967 - val_loss: 0.3267 - val_accuracy: 0.8943\n","Epoch 16/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0361 - accuracy: 0.9959 - val_loss: 0.3281 - val_accuracy: 0.8936\n","\n","Epoch 00016: ReduceLROnPlateau reducing learning rate to 1.0000000116860975e-08.\n","Epoch 17/30\n","376/376 [==============================] - 207s 551ms/step - loss: 0.0351 - accuracy: 0.9963 - val_loss: 0.3273 - val_accuracy: 0.8943\n","Epoch 18/30\n","376/376 [==============================] - 207s 551ms/step - loss: 0.0370 - accuracy: 0.9957 - val_loss: 0.3274 - val_accuracy: 0.8949\n","\n","Epoch 00018: ReduceLROnPlateau reducing learning rate to 9.999999939225292e-10.\n","Epoch 19/30\n","376/376 [==============================] - 207s 551ms/step - loss: 0.0354 - accuracy: 0.9977 - val_loss: 0.3271 - val_accuracy: 0.8936\n","Epoch 20/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0339 - accuracy: 0.9969 - val_loss: 0.3291 - val_accuracy: 0.8936\n","\n","Epoch 00020: ReduceLROnPlateau reducing learning rate to 9.999999717180686e-11.\n","Epoch 21/30\n","376/376 [==============================] - 207s 552ms/step - loss: 0.0323 - accuracy: 0.9977 - val_loss: 0.3274 - val_accuracy: 0.8943\n","Epoch 22/30\n","376/376 [==============================] - 207s 551ms/step - loss: 0.0350 - accuracy: 0.9971 - val_loss: 0.3259 - val_accuracy: 0.8930\n","\n","Epoch 00022: ReduceLROnPlateau reducing learning rate to 9.99999943962493e-12.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x480 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"sFl4HX1Yhqg7"},"source":["##**XLNET:**"]},{"cell_type":"code","metadata":{"id":"umzzH1JwhtCb"},"source":["X_train, X_val = load_X_data('XLNET')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"Xfk_h7oHGF7q","executionInfo":{"status":"ok","timestamp":1628688606133,"user_tz":-60,"elapsed":5121294,"user":{"displayName":"Will Dee","photoUrl":"","userId":"18339245303983317021"}},"outputId":"72e4cad3-340c-481d-fb90-c787352581fb"},"source":["filepath = 'Keras_Models/XLNET_best_model.epoch{epoch:02d}-loss{val_loss:.2f}.hdf5'\n","Plots_Path = 'Training_Plots/INDEP/XLNET_best_model_plot.png'\n","train_complex_model(X_train, y_train_res, X_val, y_val_res, filepath, Plots_Path, 30, 8, False, 352, 29, 'RandomNormal', 6, 12, 64, 19, 2, 8, 1024, 64, 0.2, 0.0001, 'Adam')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-35d64320-d33a-929a-2b0b-16885d22268f)\n","Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d (Conv2D)              (None, 255, 1024, 352)    296384    \n","_________________________________________________________________\n","max_pooling2d (MaxPooling2D) (None, 21, 85, 352)       0         \n","_________________________________________________________________\n","batch_normalization (BatchNo (None, 21, 85, 352)       84        \n","_________________________________________________________________\n","conv2d_1 (Conv2D)            (None, 21, 85, 64)        8132672   \n","_________________________________________________________________\n","max_pooling2d_1 (MaxPooling2 (None, 3, 11, 64)         0         \n","_________________________________________________________________\n","batch_normalization_1 (Batch (None, 3, 11, 64)         12        \n","_________________________________________________________________\n","flatten (Flatten)            (None, 2112)              0         \n","_________________________________________________________________\n","dense (Dense)                (None, 1024)              2163712   \n","_________________________________________________________________\n","dropout (Dropout)            (None, 1024)              0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 64)                65600     \n","_________________________________________________________________\n","dropout_1 (Dropout)          (None, 64)                0         \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 65        \n","=================================================================\n","Total params: 10,658,529\n","Trainable params: 10,658,481\n","Non-trainable params: 48\n","_________________________________________________________________\n","Epoch 1/30\n","376/376 [==============================] - 322s 765ms/step - loss: 0.8005 - accuracy: 0.5639 - val_loss: 0.7073 - val_accuracy: 0.5811\n","Epoch 2/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.6608 - accuracy: 0.6087 - val_loss: 0.6593 - val_accuracy: 0.6157\n","Epoch 3/30\n","376/376 [==============================] - 282s 750ms/step - loss: 0.5536 - accuracy: 0.7152 - val_loss: 0.4935 - val_accuracy: 0.7613\n","Epoch 4/30\n","376/376 [==============================] - 282s 750ms/step - loss: 0.4682 - accuracy: 0.7745 - val_loss: 0.4506 - val_accuracy: 0.8085\n","Epoch 5/30\n","376/376 [==============================] - 282s 750ms/step - loss: 0.3611 - accuracy: 0.8390 - val_loss: 0.5443 - val_accuracy: 0.7826\n","Epoch 6/30\n","376/376 [==============================] - 282s 750ms/step - loss: 0.3220 - accuracy: 0.8533 - val_loss: 0.4424 - val_accuracy: 0.8331\n","Epoch 7/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.2399 - accuracy: 0.8945 - val_loss: 0.5108 - val_accuracy: 0.8125\n","Epoch 8/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.1835 - accuracy: 0.9221 - val_loss: 0.5055 - val_accuracy: 0.8238\n","Epoch 9/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.1695 - accuracy: 0.9269 - val_loss: 0.4762 - val_accuracy: 0.8431\n","Epoch 10/30\n","376/376 [==============================] - 282s 750ms/step - loss: 0.1482 - accuracy: 0.9428 - val_loss: 0.4954 - val_accuracy: 0.8364\n","\n","Epoch 00010: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n","Epoch 11/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.0751 - accuracy: 0.9728 - val_loss: 0.5374 - val_accuracy: 0.8404\n","Epoch 12/30\n","376/376 [==============================] - 282s 750ms/step - loss: 0.0539 - accuracy: 0.9796 - val_loss: 0.5685 - val_accuracy: 0.8491\n","Epoch 13/30\n","376/376 [==============================] - 282s 750ms/step - loss: 0.0535 - accuracy: 0.9763 - val_loss: 0.6102 - val_accuracy: 0.8464\n","Epoch 14/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.0541 - accuracy: 0.9773 - val_loss: 0.6400 - val_accuracy: 0.8477\n","\n","Epoch 00014: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n","Epoch 15/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.0400 - accuracy: 0.9837 - val_loss: 0.6311 - val_accuracy: 0.8431\n","Epoch 16/30\n","376/376 [==============================] - 282s 750ms/step - loss: 0.0366 - accuracy: 0.9860 - val_loss: 0.6359 - val_accuracy: 0.8438\n","Epoch 17/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.0313 - accuracy: 0.9916 - val_loss: 0.6401 - val_accuracy: 0.8444\n","Epoch 18/30\n","376/376 [==============================] - 282s 749ms/step - loss: 0.0354 - accuracy: 0.9879 - val_loss: 0.6440 - val_accuracy: 0.8471\n","\n","Epoch 00018: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x480 with 3 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}